{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 任务：请在Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集）进行分类器练习。\n",
    "# --CSDN学院--人工智能直通车--第七班--王大恒--2018/07/29"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据文件：diabetes.csv\n",
    "\n",
    "# 属性说明：\n",
    "# Pregnancies       怀孕次数                                               离散\n",
    "# Glucose           口服葡萄糖耐受实验中，2小时的血浆葡萄糖浓度。          连续\n",
    "# BloodPressure     舒张压（mm Hg）                                        连续                                      \n",
    "# SkinThickness     三头肌皮肤褶皱层厚度（mm）                             连续\n",
    "# Insulin           2小时血清胰岛素含量（uU/ml）                           连续\n",
    "# BMI               体重指数（体重，kg/（身高，m）^2）                     连续\n",
    "# DiabetesPedigreeFunction 糖尿病家族史                                    连续\n",
    "# Age               年龄（岁）                                             离散\n",
    "\n",
    "# Outcome           输出变了/类别标签（0 或 1，出现糖尿病为 1，否则为 0）  标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#了解数据集的基本信息，有助于特征工程及机器学习模型的选择。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、导入必要工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                                  # 矩阵操作\n",
    "import pandas as pd                                 # SQL数据处理\n",
    "import seaborn as sns                               #可视化\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder      \n",
    "from matplotlib import pyplot as plt\n",
    "get_ipython().run_line_magic('matplotlib', 'inline')#图形出现在Notebook里而不是新窗口"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、加载文件中的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath=\"./data/\"\n",
    "data = pd.read_csv(dpath+\"diabetes.csv\")\n",
    "# 观察前5行了解数据每列（特征）的概况以及数据是否有缺失\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、查看数据集信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#样本数和特征数\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#特征的数据类型及是否有空值\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数值型特征的基本统计量\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "有些列最小值为0，从医学意义上来看属于缺失，无意义。\n",
    "比如：血浆葡萄糖浓度，舒张压，肱三头肌皮褶厚度，餐后血清胰岛素，体重指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#统计各特征值为0的数量\n",
    "Empty_col_names=['Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']\n",
    "\n",
    "print((data[Empty_col_names]==0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Glucose，BloodPressure，BMI 缺失较少； \n",
    "SkinThickness和Insulin  缺失太多；\n",
    "需要针对不同列的缺失情况做对应策略。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4、查看这些样本各特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用countplot查看类别型特征Outcome的直方图\n",
    "sns.countplot(data.Outcome)\n",
    "plt.xlabel(\"Diabetes\")\n",
    "plt.ylabel(\"Number\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看怀孕次数的分布\n",
    "fig=plt.figure()\n",
    "sns.countplot(data.Pregnancies)\n",
    "plt.xlabel(\"Pregnants\")\n",
    "plt.ylabel('Number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "怀孕十几次看似不合理。在疾病问题判断上，异常值可能意味着得病，需要保留。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d79308b400>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#统计怀孕次数对应的得病人数\n",
    "sns.countplot(x=\"Pregnancies\",hue=\"Outcome\",data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "可以看得出怀孕次数与是否得病关系比较密切。怀孕次数高的人群中得病的人占比较大。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\www_9\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看血浆葡萄糖浓度的分布\n",
    "fig=plt.figure()\n",
    "sns.distplot(data.Glucose,kde=False)\n",
    "plt.xlabel(\"Glucose\")\n",
    "plt.ylabel('Number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#血浆葡萄糖浓度与有无得病的人数的分布\n",
    "sns.violinplot(x=\"Outcome\",hue=\"Outcome\",y=\"Glucose\",data=data)\n",
    "plt.xlabel(\"Diabetes\")\n",
    "plt.ylabel('Glucose')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "从涂上卡血浆浓度高的人大部分都是糖尿病。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\www_9\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看舒张压的分布\n",
    "fig=plt.figure()\n",
    "sns.distplot(data.BloodPressure,kde=False)\n",
    "plt.xlabel(\"BloodPressure\")\n",
    "plt.ylabel('Number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "值为0的血压是缺失值需要填补。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y4H1gE7ABeB74n3DH56S5c+dSWlJCTVb/Qy+IUNWhH2vXrmHVqlXOBGdMO1VcXMxjjz5MlyTl3O5H78Q+XJwbftG3hG82b2HmzJlNP6EVicSop5+pahdVjVHVXFV9UVX3quoYVc0Lft0XvFdV9XpV7aOqQ1R1Ubjjc4rX62XWrFfRpA74Ursecb0mqx8SE8/MmbMciM6Y9klVeeyxR9m3bx+/GXgAzzG+Q47MquH0LpW89tqrfPXVV+EJ0gE2M9sh7733Hjt2bKeyy/DAsInDuWOo7DSYhQs/a1O/cMZEs9mzZ/PJJ5/yo95l9Eo9vsU5L88rIztR+d977mbnzp0hjtAZligcUFRUxIwZz+NL7YI3/cgRW7WqswdDfArTnnyyzbV5GhNtPv74Y1544QVOzq7i3G7Nb3I6XLwHbjqhiOqKEiZNnEhZWVkIo3SGJYoIU1WefPJJysrLqew+uuHaRC23h4rck9iyeTOzZ8+OXJDGtDOLFi1iyv330TfNx7UDShv9s2yOrkl+xg8uZvPmb7jrromHTKBtjSxRRNg///lP5s+fT1XXEfgTmp4j4c3oQU1mH2bOnMnq1asjEKEx7cuKFSv47eRJdI6v4ZahB4h1h+Z1T8is4deDSli+bDn33H13q16/zRJFBG3evJnHHn8cf0o21V2GNPt5lT1G449J5J577uXAgQNhjNCY9mXJkiXcduutpHuquH1YEckxoV3yZ3R2NVf1L2Xh558zefIkqqqOPsM7mlmiiJDy8nIm//a3VPuF8t5nHjJvokmeOMp6n8WuPXu4f8oU2wHPmBBYtGgRd95xO5kxlUwavp/0uPCsC3dWThXXDChl0ReLmHjnnZSXl4elnHCyRBEBXq+Xe+69l4KCAsp6nYnGJh3za/iTs6js9i0WffEFzz7bokV1jWn35s2bx8Q77yQ7riqsSaLWmV2rGDeohKVLl3DzTTdSVFQU1vJCzRJFmKkqTz/9NF98/jmV3U/Gl3pci+ECUNNpANXZg3jrrbf429/+FsIojWk/5syZw/3330eflCruGl7U7JnXLXVq52puHFLMpo3rGX/9/7BjR+tZdMISRZi99tpr/L//9/+o7nwCNZ1avqpkVbeT8KZ356np05k/f37LAzSmnfD7/fzxj39k+vTpjOhQze3DDhyxB3a4jehYw53DDrB/93b+57rfsG7duoiWf7wsUYTRO++8w4svvkhNhz5U5Z4YmhcVFxW9z8SfnMX9U6awaFGbnbxuTMhUV1czZcoU3njjDcbkVDJhSEnIRjcdq37pXn47oghX1QEm3HADCxcudCaQY2CJIkw++OADnpg2DW9aLpU9T298vsSxcnso63s23thUJk2azLJly0L32sa0MSUlJdxx++3MmzePH/Uu44p+ZbhC+Od4PHKSfNw9ch/ZsRXcddddvP/++84G1ARLFGEwd+5cHnroIXwpXajo+x1wheHH7ImjrN/3qHYncPsdd7By5crQl2FMK7dr1y5uGH89y5cv5TeDSrigZ2VIP7O1REacMmnEfganV/HII4/w8ssvR+2OnJYoQuzDDz9kypQp+JKzKe87Blzh2xtKYxIo7TeWKonjlltvtZqFMfVs2rSJ/7nuNxRu38LtQw9wSufom/CW4IGbhxZzeudKZs6cyWOPPRaVy/VYogih999/n6kPPog3pTNleWeDOybsZWpsIqX9zqVK4rnt9ttZvHhx2Ms0JtotX76cCTeMx1u6j8kjihiUGX1vvrU8LvjlwDIu7FHOe++9xz333B11E/MsUYTIm2++ySOPPIIvtSvlfSOTJGppbCKl/cdS5U7kjjvutJ3xTLu2cOFCbrv1FpIp4+6R++meHP0TVEXg0j4VXNGvlAX/WcDEO++Iqol5lihaSFV54YUXeOaZZ6jJ6El53++CO/JbkWtMIqX9z6U6Pp27776bv//97xGPwRinffrpp0yeNInOcVX8dsR+Oib4nQ7pmHw3t4pfDyph2dKl3HzzTZSUlDgdEmCJokW8Xi8PPfQQr732GtVZ/ajscya4HBpzB+CJp6zfWGqSO/Pwww8za9asqO0cMybUPv74Y+699x56Jldz14jITaQLtVM6VzNhSDEb1q/jtltviYpk0axEISL9RGSuiKwIPh4qIr8Nb2jRrbS0lDsnTuSDDz6gqusIqnqcemzrN4WLO4byvLOp6dCHl156KWo7x4wJpQULFnDfff9L7+Qabh92gERPeJLEa+sS2VziZnOJm6mLU3ltXWJYyhnRsYYJJxSzccN6br/tVseboZr7zvY8cBdQA6Cqy4CftrRwEblZRFaKyAoReV1E4kWkl4gsFJH1IvJnEYltaTmhVlhYyPXjx/Pll4up6Hka1TkjQjtPoqVcbip7nUFVl6G899573DlxIqWlpU5HZUxYLFu2jHvvuYfuSTXcNuwACWFKEgBbSj1U+FxU+FysKYphS2n4mplHdKxh/OBi1q1bx92/+62jy5Q3N1Ekqurnh51r0cdUEckBJgD5qnoC4CaQfB4GpqlqHrAfuLYl5YTa2rVr+fVvfsOWgu2U552NN6uf0yE1TITq3Hwqe57Gl18u5vrx4yksLHQ6KmNCqqCggEl3TaRDbDW3Dg1vknDCyKwarh1QyqIvF/Poo4861pTc3ESxR0T6AAogIpcCoVjRygMkiIgHSAy+5neAt4LXZwIXh6CckPj3v//NDTdMoKjCS+mA7+NLy3E6pCbVZPWjPO9sthRs59e//g1r1651OiRjQqKsrIzJd02EmnJuG9p6+ySacnqXKi7pVc5HH33EW2+91fQTwqC5ieJ64DlggIhsA24CftOSglV1G/AYsIVAgjgAfAkUqWptbaUAiIp34zlz5vDb3/2OythUSgec36zd6aKFLy2H0gHfp6jSyw03TGDBggVOh2RMiz3++ONsLShg/OADZLWy0U3H6sKeFYzKqubZZ55xZBWGJhOFiLgINA99F8gCBqjqaaq6uSUFi0gGcBHQC+gKJAHnNnBrgx8TRGSciCwSkUW7d+9uSSiNqr/ipDe9G2X9zkVjEsJWXrj4EzIoHXA+lTGpTJ48mXfffdfpkIw5bvPnz2fevHlc3LOcQRltf7CGS2DcwFIy4pWpD0yhoqIisuU3dYOq+oHxweMyVQ3VWK3vAl+r6m5VrQHmAKcA6cGmKIBcYPtR4pqhqvmqmp+VlRWikA7l8/l4+OGHeeONN6juNICKPt8JyxyJuC2f4S7fi7t8Lwlr3iduy2chLwMCS36U9R+LNzWHxx57jNdeey0s5RgTTuXl5Tw57Ql6pfq4oEdk3zCdlOBRfjXgANu272D27NkRLbu5TU8fichtItJNRDJr/7Ww7C3AaBFJFBEBxgCrgH8BlwbvuRJ4u4XlHBev18sDDzxwcPhr95PDNvzVVb4P8dUgvho8JTtxle8LSzlAYPhs3+9Sk9mHF154IaoXIjOmIW+++SZFB4q5Iq8UdxSMSI+kQRleRneq4i9/eZO9e/dGrNzm/pivIdBP8QmBfoQvgRZthKCqCwl0Wi8GlgdjmQHcCdwiIhuADsCLLSnnePj9fh588EHmzZtHVW5+9A1/bSmXi8rep1PTMY+ZM2fy6quvOh2RMc1SVVXFX978M6OyquiT1vabnBryw97l1FRXM2fOnIiV2ax2FFXtFY7CVfUe4J7DTm8CTgpHec31/PPPM3fuXKpyRlHdZaiToYSPuKjseRqon5deeolOnToxduxYp6MyplELFiygrLyCMf2ia9G8SMpO9HNCZjUffvAPrr32Wlzh2MbgMM1KFCJyRUPnVXVWaMNx3ocffsjrr79OddaAtpskaolQ2fM0XDUVPPLoo3Tr1o3Bgwc7HZUxR7VgwQLS4mBQRo3ToTjq5Owqnlu1l/Xr19O/f/+wl9fcVHRivX+nA/cCF4YpJsfs3LmTadOexJ+STVWP0W2rueloXG7K+5yF35PAlAceiPhoCmOOxcb16+iZXO34DnVO650SaHbbtGlTRMprVqJQ1Rvq/fsVMAKIuqU1WurJJ5+kssZLea8zomPdpkjxxFHe83R2bN/BrFltrpJo2pCdhYV0SnBu2fAKrxAfH8+ll15KfHw8FV5nMlan4LyRSK22cLzvhuVAXigDcdratWv57LPPqMwegsalOB1OxPlSu1CT2Zs5c/5GUVGR0+EY0yCPx4NfnatOlHuF888/n/Hjx/P973+fcocSRe04RY8nMlsaNLeP4v84GJsLGAS8Ga6gnPDWW28hnjiqswc6HYpjqrsOo2rFRt577z0uu+wyp8Mx5gipqansrwrj8PEmJHqUd999F1XlvffeI9uhtaX2VwU+46ekROZDbXPT0WP1jr3AZlUtCEM8jvD7/Xy28HOq03LB3eZa1JrNn5COJnXks4ULLVGYqDRk6DA+nbsDv5Y40k+R4FEqSyv561//Gnic7kyiWFsUeOseMmRIRMprbtPTIuBTVf0Y2A2MFJHI7fUZZlu3bqWk+ADe1K5Oh+K4mpQurFy50vawMFEpPz+f0mpl+b428/ZzXP5TGE+HzAx69uwZkfKamyg+AeKDS4PPBa4GXglXUJG2f/9+ADQ2yZkAfNWHdJDhc27deX9sEn6fz/avMFHpjDPOoGOHTN7bnEh7XVDgmxI3K/fF8MNLfxSRORTQ/EQhqloOXAL8XlV/QKCfok2oHRKqrsjvdQ0g3upDOsjE61yiqP0Z2DBZE41iYmL4+WW/YE2Rh/8Wtr9mYp8fXlmbQmpKMhdccEHEym3uO6OIyMnAZRzcSMiZd9Uw6NChAwCumgqcWKxYPbGHdJCpJzzbKzaHqyaQIDIyWs8y6qZ9ueiii/jnRx/y2oY1DEjfT2Z8215ivL73t8SzqdjN7353c8Q6sqH5NYqbCGyF+jdVXSkivQks3tcmdOnSBQBXxX5nAnDHUlkZ6CCrrKx0tEPdVVFEWnpGoAnMmCjkdruZeNckfK44pi1Po7KddKd9tSeGtzYlceaZZ/Kd73wnomU3d8Ldx6p6oao+HNyfYo+qTghzbBGTkpJCnz598RQ3uKJ5+6FKbOl2Tswf5XQkxjSqe/fu3HPv/7K11M0zK1PwtvFKxcZiD8+sSqNfvzwmTpyIRHjViGYlChH5k4ikikgSgaXA14rI7eENLbJOPfUU3KWFSFWotttofdzF29HqCk4++WSnQzGmSaNHj+bGm25iyd5Ynl7RdpPFxgMeHlmaRmaHLKY++JAjtf3mNj0NUtViAvtXvw90By4PW1QOOP/883GJi9jCVU6H4pjYwpWkp2dw+umnOx2KMc1y0UUXMWHCBBbvieWp5SlUObe6R1is2R9IEukdsnnq90/X9adGWnMTRUxw3sTFwNvBHena1OC0Tp06MWbMd4jbsxapan9DQ93FO/AcKODSS39IbGz7G01iWq9LLrmEW265heX74njwq3SKq9vGioGfFcbyyNI0srrk8tT039OpUyfHYmluongO+IbAvtafiEgPoDhcQTnll7/8JTFuN/FbP6NdDdL2+0nY+hmdOmVz6aWXNn2/MVHmwgsv5L7776egIp77Fmewvaz1LuqpCu9ujueZlSkMHDSYp//wjKNJAprfmT1dVXNU9TwN2AycFebYIi47O5urr74Kz/4txOxZ73Q4ERNX8AVSvp+bbrrRRjuZVuu0005j2pNPUh2Txv8uzmDp3tDP3u6e7CXB7SfB7WdAeg3dk0M75KraB8+tSubNjUmcddZZPPb4E6Smpoa0jOPR3M7sbBF5UUT+Hnw8iMB+1m3Oj3/8Y0aOHEnClv/iKtvjdDhh59n3NbGFK/nBD37AKaec4nQ4xrTI4MGDeW7G8+R0780Ty1J5b3N8SBsHftGvnB4pPnqk+Jg0sphf9CsP2Wvvq3Ix9at0FhTGce2113L33XcTFxcXstdviebWz14BPgBqF0NaR2BuRYuISLqIvCUia0RktYicLCKZIvKRiKwPfo3ozC+3283dd99NZmYmyRv+iVS2uRa2Ou6SnSR+/Sn9BwzguuuuczocY0IiOzub3z/9B84449v8eWMSz65MjvpO7rVFHu5ZlMGO6kTuv/9+Lr/88ogPgW1McxNFR1V9EwITl1XVC4TiR/8U8A9VHQAMA1YDE4FYU1WmAAAbi0lEQVS5qppHYF2piSEo55ikp6fzxOOPkRTnJnn9B22yc9tVtoekDf8kJ6cLDz/0kHVgmzYlISGBe++9l3HjxrFwdxz3Lc5gd0V09lvM2xbHQ1+lkdyhC8/+8bmoHHXY3J9cmYh0IDjSSURGAwdaUrCIpAJnAC8CqGq1qhYBFwEzg7fNJDDSKuJ69OjBY48+SoL4SF77Pq6KtrOZj7t4O8lr/05WZgZPPP446enpTodkTMiJCD//+c95+OFH2O9P5p4vM1i5L3pWHqrxw8trknhlbTL5J57EH5+bEbHVYI9VcxPFLcA7QB8R+Q8wC7ihhWX3JrBk+csi8pWIvBCc0JetqjsAgl8d6+4fMGAA06c/RVqCJ5AsSneFpRx/YibqjkHdMXhTOuNPzAxLOQCefZtIXP8R3bvl8Mwzf3B8NIUx4XbSSYE34Q6du/Ho0jQ+KnB+wEZxtfDwkjT+tT2eyy67jKkPPhjRtZuOVZOJIrhkRzzwbeAU4NfAYFVd1sKyPcBI4FlVHQGUcQzNTCIyTkQWicii3bt3tzCUo8vLy+OZP/yB7I4ZJK39O54wjIaq6j4aX2IHfIkdqBhwHlXdR4e8DFSJ3baYhI3zGTRwAL+fPp2OHTuGvhxjolBubi7P/vE5Ro8+mVfXJTFzbRI+h2ZyF5S6+d8vM/imLIG7776bX/3qV7jdbmeCaaYmE4Wq+oHHVdWrqitVdUVwwl1LFQAFqrow+PgtAomjUES6AAS/NvgxXlVnqGq+quZnZWWFIJyjy8nJYcZzzzFi2DASvv6UuC0Lwd+K1gvwVpOwcS5x25cwduxYnpw2LSqG3BkTSYmJidw/ZQo/+clPmLstnmnLUyPeyb1qv4f7v0rHF5/BU9OnR3xxv+PV3KanD0XkhxLCbnhV3QlsFZH+wVNjCKwj9Q4Hh95eCbwdqjJbIjU1lUcffYRLLrmE2MKVJK37O1Jd5nRYTXKV7yNlzTvEHijg+uuv584777SOa9Nuud1urrvuOm699VZW7IuN6EzuhYWxPLY0jU5duvHH52YwcODAiJQbCs3t2bmFwKxsr4hUAgKoqrb0Y+kNwGwRiQU2Edg5zwW8KSLXAluAH7WwjJDxeDxMmDCBwYMH88gjj+Je/Q5lPc/Al5bjdGgN8uxeR+LWz0hLTeW+h59k6NChTodkTFS44IILSE9P5/777mPqVy4mDt9Pelz4VmP4dEccL6xOZvDgwUx98MFWV6Nv7szsFFV1qWqsqqYGH7f4O1XVJcHmo6GqerGq7lfVvao6RlXzgl/3tbScUBszZgwzZjxHbucsEtd9QOy2xaBR1BTlqyF+0yckfPNvhg05gZdefMGShDGHOf3003nk0UfZ643joSUZFFWFp2bx8fZAkhg1aiSPPf54q0sS0ESiEJFOIvKkiLwrIlODQ1oNgeGzM557jnPOOYe47UtIXPcBUuP89qGuiiKS17xLzL6NXHnllTz++ONkZoZvFJUxrdnw4cN55JFH2eeN45Gl6ZTVhDZZLCyM5aU1yeSfmM8DUx9stUvkNFWjmEVgNNLvgRRgetgjakUSEhKYNGkSd955J/EVe0he9TbukkLH4vHs3UTy6v8j1ePnsUcf5eqrr4760RTGOG3YsGE8MPVBdlbE8NTyVKpD1MG9ar+H51ancMIJg5ky5YGoWY7jeDSVKDqr6mRV/UBVbwCs/aIB5557Ls8++yxdOqSTuPbvxBSuiuzqs+onbstCEjbNZ2D/frz04gvk5+dHrnxjWrlRo0Zx16RJrCny8Oq6pBa/3u4KF79fkUZObjemPvhQq04S0HSiEBHJCK6/lAm4D3tsgvr27cvzz8/g5JNHE7/lM+I2LwB/BMbeeatIXP9R3cJ+06c/RbiHCxvTFo0ZM4YrrriCj3fE869tx//GXu2D6SvSkNhEpj74UFRPpGuupkY9pQFfEhjlVGtx8KsSmF1tgpKTk3lgyhRefPFFZs+ejbuqmPI+Y8ATnuGoUlVK8voPcVeXcPNtt3H++eeHpRxj2osrr7ySVatW8tpXXzIgvYYuScc+SOUvmxLZXOLiwQd/R05OdI6IPFaN1ihUtaeq9lbVXg38syTRAJfLxa9+9SsmTZpETOkuktf9HakJ3VLEdeWU7yd57Xskump4/PHHLUkYEwJut5u77ppEfEISz69JxX+MLchrizx8uDWBiy++uE3tPd/UqKeRjf2LVJCt0TnnnMODD04lrqaU5DXvh3RynqtsD8nr3icjKY6nf/97hg8fHrLXNqa969ChAzfedDMbDriZv735TVBeP7yyLoXO2Z349a9/HcYII6+pPorHg//+ACwEZgDPB49tBFQTvvWtbzFt2hPESw3JIRo+6yrfR/L6D+mQnsozf/gDvXtbxc6YUBszZgzDhg7lra+Tmz1kdt62eLaVurj+hgkkJCSEOcLIaqrp6SxVPQvYDIwMTo4bBYwANkQiwNZu8ODBPPLww8T6Kkha9wH4jn+ZrNo+iYzUJJ568km6dOkSwkiNMbVEhPE33EBpNfxja9NzH6p88M6WJEYMH86pp54agQgjq7lrPQ1Q1eW1D1R1BWDtHc00dOhQHnhgCu7KIhI2zT++Wdy+apI2/JN4D0x74ok200lmTLTKy8vjjDPO4KNtiU3WKv61LZ7iKrjm2mujame6UGluolgd3C/iTBH5tog8T2A3OtNMJ554IhMmTMBTtJXYbV8d25NVSfj6U9yVRdx/33306NEjPEEaYw5x+eWXU14Dn+w4el+Fzw8fbEti2LChDBkyJILRRU5zE8XVwErgRgJ7Za8KnjPH4OKLL2bs2LHE7ViKu3hHs58Xs3stnv2bGTdunE2kMyaC8vLyGDrkBP65PbFuBFT3ZC/dk7119yzZG8PeCrj00qhZvzTkmrsoYCWBDu27gd8BTwfPmWM0YcIEuubkkPjNp83qr5DKYhIKPmdUfj4//vGPIxChMaa+iy7+AbvLhTVFgWlnv+hXzi/6HRzy/smOeDp2yGxTw2EP16xEISJnAuuBp4FngHUickYY42qzEhMTmTxpElSVEtdUE5Qq8Vs+IzbGw5133IHLFZ2bwxvTlp166qkkJsTzn51HNj+VVAvL9sZy9jnfw+OJnv24Q6257zyPA+eo6rdV9Qzge8C08IXVtg0ePJjvf//7xO5aiVQcOOp97gNb8Rwo4JfXXmt7WxvjkPj4eE47/QwW74nHe9g4lMV7YvEprWanuuPV3EQRo6prax+o6jogJjwhtQ+//OUviYuNI2774oZvUCVh22K6dO3KD37wg8gGZ4w5xGmnnUZZDaw7cGitYfGeWLI7ZdG3b1+HIouM5iaKRSLyYnDU05nBUU9fhjOwti4jI4Mf/ehSYvZ9jaui6IjrnqItSPk+rrn66jZdpTWmNcjPz8ftdrF878F127x+WFUUx+iTT2mTQ2Lra26iuI7AqKcJBEY+rQJ+E66g2osf/vCHuD0eYnatOuJa7K5VdMzK4qyzznIgMmNMfYmJiQwaOIhVRQcTxcZiD1VeZdSoUQ5GFhnNHfVURaAj+x4OjnqqCkUAIuIWka9E5N3g414islBE1ovIn4P7abdJGRkZfHfMGOL2bjxk/wpXRRHu4h388JJLrDZhTJQYNnw4m0vcVAV3D1gfbIZqD9sMR8Oopxs5dPLew8A0Vc0D9gPXhqicqDR27FjUV4N4D4429uzbhIhwzjnnOBiZMaa+QYMG4Vf4piSQIDYeiCGnS2fS09Mdjiz8HB31JCK5wPeBF4KPBfgO8FbwlpnAxS0tJ5oNGzaMjMzMQxJFXNFmhg0bRocOHRyMzBhTX//+/QHYHEwUW8pj6TdgoJMhRYzTo56eBO4AageddQCKVLV22mMB0KYXNXK5XJw8ejQuXzWoIlWlUL6fU045xenQjDH1ZGZmkpaSzNZSNxVeYXc57Wb1ZsdGPYnI+cAuVa3/Og0NHWhw6xARGScii0Rk0e7du1sSiuNGjRoVSBJ+L+6SnQfPGWOihojQvUdPdlZ42FkeeOtsL+uuOTnq6VTgQhH5BniDQJPTk0C6iNT24OYC2xt6sqrOCC57nt/a94geNGgQAOKrwV22m7i4eHr27OlsUMaYI+Tk5rKr0kNhhTvwuJ2s4tzsUU+q+oSqXqKqP1DVaS0d9aSqd6lqrqr2BH4KzFPVy4B/AZcGb7sSeLsl5bQGnTt3xuV2g68Gd/k+8vLycLvdTodljDlMdnY2RZWwK5goOnfu7HBEkdHo2EsRWc5Rmn4AVDUc48LuBN4QkSnAV8CLYSgjqogI6Wnp7CmpwFN1gJ49T3Q6JGNMA7KyslDg62IPCfFxJCUlOR1SRDQ1SP/8SAShqvOB+cHjTcBJkSg3mpx00on846O5qK+G3Nxcp8MxxjQgMzMTgK1lbjIzMhyOJnIaTRSquvnwcyLSEdirqketaZhjl5WVVbfseGvvczGmrUpLSwMCTU+De2U6HE3kNNpHISKjRWS+iMwRkREisgJYARSKyNjIhNg+1H5SgcCMbWNM9ElNTa07Tql33NY11fT0NDAJSAPmAeeq6mciMgB4HfhHmONrN1JSUho8NsZEj/p9Eu2lfwKaHvXkUdUPVfUvwE5V/QxAVdeEP7T2JTExse64Pf0CGtOa1P87rX/c1jWVKOpv01Fx2DXrowihhISEuuP4+HgHIzHGHE1cXFzdkuLt6e+0qaanYSJSTGDGdELwmODj9vNTioDY2NgGj40x0UNEiI3xUFVdQ1zckVujtlVNjXqyWV8RUn+CnS0tbkz0Cvyt1rSrD3TNXcLDhFn9RGGzso2JfpYoTMTVTw4ul/23GBO1gn0UlihMxNXfc7et779rTKsWHMYTExOKnRZaB0sUUaJ+LcJqFMZEv/bUl2jvSMYYcyyCFX6rUZiIs1qEMa1DbdOw1ShMxFm/hDGtg98f6KSwRGEizmoUxrQOVqMwjrFEYUzrYonCRJwlCmNal/Y0MdbenaJEe/qlM6Y1s6anCBKRbiLyLxFZLSIrReTG4PlMEflIRNYHv7aLXXza0y+dMW1Be2oFcPI79QK3qupAYDRwvYgMAiYCc1U1D5gbfNzmWaIwpnWo3QW6PbUCOJYoVHWHqi4OHpcAq4Ec4CJgZvC2mcDFzkQYWZYojGldrEYRYSLSExgBLASyVXUHBJIJ0Mm5yCLHEoUxrUN7nPPkeKIQkWTgr8BNqlrc1P31njdORBaJyKLdu3eHL8AIaU/VWGPagtomqPbA0UQhIjEEksRsVZ0TPF0oIl2C17sAuxp6rqrOUNV8Vc3PysqKTMDGmHavNkH4/f4m7mw7nBz1JMCLwGpVfaLepXeAK4PHVwJvRzo2Y4w5mtpE4fP5HI4kcpxsGD8VuBxYLiJLgucmAQ8Bb4rItcAW4EcOxWeMMUeo7aOoqalxOJLIcSxRqOq/qVuw9whjIhmLMcY0V22NoqKiwuFIIsfxzmxjjGmNLFEYY4w5ikCNori42YM0Wz1LFMYYcwxqRzsVFRU5HEnkWKIwxphmKi8vp6qqGoC2MH+ruSxRRIn2NHnHmNaqsLCw7njXrsJG7mxbLFFEierqaqdDMMY0oaCgAIDOiT4KtmxxOJrIsUQRJdpTx5gxrdXmzZsBGNGxmsLde6isrHQ4osiwRBEl6neMVVVVORiJMeZoNmzYQFYi9EurQVXZuHGj0yFFhCWKKLFz58664/rtoMaY6LF2zWp6JFXRIyWwfMe6descjigyLFFEiW3btjV4bIyJDvv372fHzkL6pnnpEOcnPR5WrlzpdFgRYYkiSmzatAncMQePjTFRZcWKFQDkpXkRgbyUKpYvW+pwVJFhiSJKrFq9Bm9yNsSnsmbNGqfDMcYcZsmSJcS6oVeKF4D+6V4Kd+1mx44dDkcWfpYoosCBAwco2LoFX3I2NYlZLFu+3OZVGBNlvlr8JX1Ta/AE3zUHZQRWj12yZEkjz2obLFFEgaVLA9VXX0o2vpRsDhQVsaUdjdE2JtoVFRWx6etv6pIDQNckHymxlihMhHzxxReIJxZfUie8aTkALFq0yOGojDG1aj/MDayXKFwCA9KqWPxl2/9btUThMFVl4edfUJ3cGVwuNC4FEtL44osvnA7NGBO0bNmyQ/onavVP97J7z95Dhre3RZYoHLZt2zZ2Fe7EF6xJAFSndGXx4q9sWQ9josSKFcvpneKt65+o1S89UMNo68NkLVE47KuvvgLAm9K17pwvtSvV1VU2+smYKOD1etm0aRO9Uo/c+jQ3yUeMq+1PvIvaRCEiY0VkrYhsEJGJTscTLsuWLUNiE9H41LpzvuRsAJYvX+5UWMaYoG3btlFT46V7sveIax4X5CT72/zcp6hMFCLiBv4AnAsMAn4mIoOcjSo81q/fQE1CB5CD24drTDwkpLJhwwYHIzPGwMGVEjon+Bu83im+hu0FWyMZUsRFZaIATgI2qOomVa0G3gAucjimkPP7/WzbVoA/Pu2Ia97YVDbbEFljHLdnzx4AMuIbThQd4vzs2bcvkiFFXLQmihygfoouCJ5rUyorK6mpqcEfk3DENX9MAkVFBxyIyhhTX1lZGQBJnoYTRaJHqaqqxus9smmqrYjWRCENnDtkqrKIjBORRSKyqLVuSVi3nLjLfeRFl4dqW27cGMfV1AQ6sd0NvSsBbpcecl9bFK2JogDoVu9xLrC9/g2qOkNV81U1PysrK6LBhUpcXFzgwO874pr4vcTFx0U4ImPM4WJjYwHwNlyhwOsPZJCYmJhIhRRx0ZoovgDyRKSXiMQCPwXecTimkEtMTCQ2Ng5XTfkR16SmnMzMTAeiMsbUl5ycDECZt+G3yzKvEB8Xi9vdQMtAGxGViUJVvcB44ANgNfCmqrbJGS253XJxVRQdcT6m6gA9und3ICJjTH0dO3YEYF9Vw2+X+6tcZGV1ROQobVNtQFQmCgBVfV9V+6lqH1V9wOl4wmVA//7EVOyFeqvFSk0FWllKXl6eg5EZYwBycgLjaHaUNVxj2FERQ9ecbg1eayuiNlG0F0OGDEFrKg+pVbhLdtRdM8Y4q2vXrsTFxrC1gUTh9cP2Mhd9+vRxILLIsUThsBEjRgDgLj7YV+8u3k5CQiL9+vVzKixjTJDb7aZvXh6bio/srN5c6sHnp83/rVqicFjnzp3Jyc3FU1wQOKFKbPF28vNH4fF4nA3OGAPACScM4esSD9WHDVBcX+QJXj/BgagixxJFFBj9rW8RU1IIfi+uygNQVcqJJ57odFjGmKBhw4bh9cOm4kM/vK0uiiGna+e6Du+2yhJFFBg1ahTq9+Iu3V3XP5Gfn+9wVMaYWkOGDMElwqr9B5uf/ArrDsQxfMQoByOLDEsUUWDYsGGICO6SHbiLd9AxK4suXbo4HZYxJiglJYW+ffuwuii27tzmEjdlNVrXz9iWWaKIAklJSfTo2Qt32W5iK/YwdMiQNj0m25jWaMTIUWwoPthPsbooULsYPny4g1FFhiWKKDFo4AA8B7ahlaUMGDDA6XCMMYcZNmwYPj9sDPZTrC2KIadrlzbfPwGWKKJGr1696o579uzpXCDGmAbVzmtaVxSDKqwvjmXosLZfmwBLFFGjd+/edceWKIyJPikpKXTLzWFTiYddFS5Kq2HQoDa5n9oRbKB+lBg5ciTTp08nISGBTp06OR2OMaYBAwYO4otPt/FNSWALgP79+zscUWRYjSJKiAhDhw619Z2MiWJ9+vShqBLWFMXgcrno0aOH0yFFhCUKY4xpptpm4S92xZLTtfPBPWXaOEsUxhjTTLUryRbXuMjJbT/bAFiiMMaYZurcuTOJiYE97tv6irH1WWe2McY0U0xMDH/60+sUFRWRm5vrdDgRY4nCGGOOQXp6Ounp6U6HEVHW9GSMMaZRjiQKEXlURNaIyDIR+ZuIpNe7dpeIbBCRtSLyPSfiM8YYc5BTNYqPgBNUdSiwDrgLQEQGAT8FBgNjgWdEpOGNao0xxkSEI4lCVT9UVW/w4WdAba/QRcAbqlqlql8DG4CTnIjRGGNMQDT0UVwD/D14nANsrXetIHjOGGOMQ8I26klE/gl0buDSZFV9O3jPZMALzK59WgP361FefxwwDqB79/Yz8cUYYyItbIlCVb/b2HURuRI4HxijqrXJoADoVu+2XGD7UV5/BjADID8/v8FkYowxpuXk4Ht0BAsVGQs8AXxbVXfXOz8Y+BOBfomuwFwgT1V9TbzebmBz+CJudzoCe5wOwpgG2O9maPVQ1aymbnIqUWwA4oC9wVOfqepvgtcmE+i38AI3qerfG34VEy4iskhV852Ow5jD2e+mMxxJFCa62R+jiVb2u+mMaBj1ZIwxJopZojANmeF0AMYchf1uOsCanowxxjTKahTGGGMaZYnC1BGRscHFGDeIyESn4zGmloi8JCK7RGSF07G0R5YoDADBxRf/AJwLDAJ+Flyk0Zho8AqBhUKNAyxRmFonARtUdZOqVgNvEFik0RjHqeonwD6n42ivLFGYWrYgozGmQZYoTK1mL8hojGlfLFGYWs1ekNEY075YojC1vgDyRKSXiMQS2GnwHYdjMsZEAUsUBoDgjoPjgQ+A1cCbqrrS2aiMCRCR14H/Av1FpEBErnU6pvbEZmYbY4xplNUojDHGNMoShTHGmEZZojDGGNMoSxTGGGMaZYnCGGNMoyxRGBMkIj4RWSIiK0VkqYjcIiKu4LV8EZnexPOvEpGnj7HMSS2J2ZhIsOGxxgSJSKmqJgePOwF/Av6jqvc08/lXAfmqOv54yjQmWlmNwpgGqOouYBwwXgLOFJF3AUTkJBFZICJfBb/2r/fUbiLyj+C+HnUJRkR+ISKfB2ssz4mIW0QeAhKC52Y3cp9bRF4RkRUislxEbo7kz8IYj9MBGBOtVHVTsOmp02GX1gBnqKpXRL4LTAV+GLx2EnACUA58ISLvAWXAT4BTVbVGRJ4BLlPViSIyXlWHA4jIwIbuA1YCOap6QvC+9HB+38YczhKFMY1raFXdNGCmiOQRWGE3pt61j1R1L4CIzAFOA7zAKAKJAyAB2NXA6445yn3/B/QWkd8D7wEftvzbMqb5LFEYcxQi0hvwEXizHljv0v3Av1T1ByLSE5hf79rhnX5KINnMVNW7miryaPeJyDDge8D1wI+Ba5r9jRjTQtZHYUwDRCQL+CPwtB454iMN2BY8vuqwa2eLSKaIJAAXA/8B5gKXBjvICV7vEby/RkRqayQN3iciHQGXqv4V+B0wMmTfqDHNYDUKYw5KEJElBJqSvMCrwBMN3PcIgaanW4B5h137d/B5fYE/qeoiABH5LfBhsM+jhkDNYDMwA1gmIotV9bKj3FcBvFw7VBdoqmZiTEjZ8FhjjDGNsqYnY4wxjbJEYYwxplGWKIwxxjTKEoUxxphGWaIwxhjTKEsUxhhjGmWJwhhjTKMsURhjjGnU/wcCgVa3d0AsCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看舒张压与是否得病（标签）之间的关系\n",
    "sns.violinplot(x=\"Outcome\",hue=\"Outcome\",y=\"BloodPressure\",data=data)\n",
    "plt.xlabel(\"Outcome\")\n",
    "plt.ylabel('BloodPressure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of SkinThickness')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看三头皮褶厚度的分布情况\n",
    "plt.scatter( range(data.shape[0]),data['SkinThickness'])\n",
    "plt.title('Distribution of SkinThickness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#有个别异常值，因为是病例问题所以不删除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\www_9\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看三头皮褶厚度\n",
    "fig=plt.figure()\n",
    "sns.distplot(data.SkinThickness,kde=False)\n",
    "plt.xlabel(\"SkinThickness\")\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#为0的值比较多。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看三头皮褶厚度与是否得病的关系\n",
    "sns.violinplot(x='Outcome',hue=\"Outcome\",y='SkinThickness',data=data)\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('SkinThickness')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从图上看感觉相关性不是特别强。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\www_9\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAFo1JREFUeJzt3X2QZXV95/H3J4BglHVAWjLODA7GIYpWOWgvYthKKZhVKRMwixvQRMqazWS3MD7uRtCtilYtW1plxJBNkYwBHS0V8SnMsgSCPGiZjYODThAYWUZgocOEaZYnH0p2B7/7x/01XMcz3RdmTt+e7ver6tY953d+59zvPXWnP3OeU1VIkrS7Xxp3AZKkhcmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdeg+IJAck+W6Sy9v40Uk2J7k9yReSPK21H9zGt7fpq/uuTZK0Z/OxBfFOYNvQ+EeA86tqDfAgsK61rwMerKoXAOe3fpKkMUmfV1InWQlsBM4D3gP8FjAN/EpV7UrySuCDVfXaJFe14X9IciDwz8BEzVLgEUccUatXr+6tfklajG688cb7q2pirn4H9lzHx4E/Bg5t488GHqqqXW18CljRhlcA9wC08Hi49b9/TwtfvXo1W7Zs6aNuSVq0kvzvUfr1tospyRuAnVV143BzR9caYdrwctcn2ZJky/T09D6oVJLUpc9jECcCv53kLuAS4CQGWxTL2i4kgJXAvW14ClgF0KY/C3hg94VW1YaqmqyqyYmJObeQJElPUW8BUVXnVtXKqloNnAFcW1VvAa4DTm/dzgIua8Ob2jht+rWzHX+QJPVrHNdBvA94T5LtDI4xXNTaLwKe3drfA5wzhtokSU3fB6kBqKrrgevb8B3A8R19fgq8aT7qkSTNzSupJUmdDAhJUicDQpLUyYCQJHWal4PUC9HnNt89Ur83v+KoniuRpIXJLQhJUicDQpLUyYCQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUqfeAiLJIUluSPKPSW5J8qHW/qkkdybZ2l5rW3uSXJBke5Kbkrysr9okSXPr83bfjwInVdWPkhwEfDPJ37Zp/6mqvrRb/9cDa9rrFcCF7V2SNAa9bUHUwI/a6EHtVbPMcirw6Tbft4BlSZb3VZ8kaXa9HoNIckCSrcBO4Oqq2twmndd2I52f5ODWtgK4Z2j2qdYmSRqDXgOiqh6rqrXASuD4JC8BzgVeCPxL4HDgfa17uhaxe0OS9Um2JNkyPT3dU+WSpHk5i6mqHgKuB15XVTvabqRHgU8Cx7duU8CqodlWAvd2LGtDVU1W1eTExETPlUvS0tXnWUwTSZa14acDrwG+P3NcIUmA04Cb2yybgLe2s5lOAB6uqh191SdJml2fZzEtBzYmOYBBEF1aVZcnuTbJBINdSluBf9/6XwGcAmwHfgK8rcfaJElz6C0gquom4LiO9pP20L+As/uqR5L05HgltSSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnq1FtAJDkkyQ1J/jHJLUk+1NqPTrI5ye1JvpDkaa394Da+vU1f3VdtkqS59bkF8ShwUlW9FFgLvC7JCcBHgPOrag3wILCu9V8HPFhVLwDOb/0kSWPSW0DUwI/a6EHtVcBJwJda+0bgtDZ8ahunTT85SfqqT5I0u16PQSQ5IMlWYCdwNfAD4KGq2tW6TAEr2vAK4B6ANv1h4Nl91idJ2rNeA6KqHquqtcBK4HjgRV3d2nvX1kLt3pBkfZItSbZMT0/vu2IlST9nXs5iqqqHgOuBE4BlSQ5sk1YC97bhKWAVQJv+LOCBjmVtqKrJqpqcmJjou3RJWrL6PItpIsmyNvx04DXANuA64PTW7Szgsja8qY3Tpl9bVb+wBSFJmh8Hzt3lKVsObExyAIMgurSqLk9yK3BJkv8CfBe4qPW/CPhMku0MthzO6LE2SdIceguIqroJOK6j/Q4GxyN2b/8p8Ka+6pEkPTleSS1J6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOvUWEElWJbkuybYktyR5Z2v/YJJ/SrK1vU4ZmufcJNuT3JbktX3VJkmaW2/PpAZ2Ae+tqu8kORS4McnVbdr5VfXR4c5JjgXOAF4MPBf4WpJjquqxHmuUJO1Bb1sQVbWjqr7Thn8IbANWzDLLqcAlVfVoVd0JbAeO76s+SdLs5uUYRJLVwHHA5tb09iQ3Jbk4yWGtbQVwz9BsU8weKJKkHvUeEEmeCXwZeFdVPQJcCPwqsBbYAfzpTNeO2atjeeuTbEmyZXp6uqeqJUm9BkSSgxiEw2er6isAVXVfVT1WVT8DPsETu5GmgFVDs68E7t19mVW1oaomq2pyYmKiz/IlaUnr8yymABcB26rqY0Pty4e6vRG4uQ1vAs5IcnCSo4E1wA191SdJmt2cZzElObyqHngKyz4R+H3ge0m2trb3A2cmWctg99FdwB8CVNUtSS4FbmVwBtTZnsEkSeMzymmum9sf+E8Cf1tVv3BcoEtVfZPu4wpXzDLPecB5oyxfktSvUXYxHQNsYLA1sD3Jf01yTL9lSZLGbc6AqIGrq+pM4N8BZwE3JPl6klf2XqEkaSxGOQbxbOD3GGxB3Af8EYMDymuBLwJH91mgJGk8RjkG8Q/AZ4DTqmpqqH1Lkr/spyxJ0riNEhC/tqcD01X1kX1cjyRpgRjlIPXfJVk2M5LksCRX9ViTJGkBGCUgJqrqoZmRqnoQeE5/JUmSFoJRAuKxJEfNjCR5Hh33SJIkLS6jHIP4APDNJF9v478BrO+vJEnSQjBnQFTVlUleBpzA4Mrod1fV/b1XJkkaq1GfKHcw8EDrf2wSquob/ZUlSRq3US6U+wjwu8AtwM9acwEGhCQtYqNsQZzG4FqIR/suRpK0cIxyFtMdwEF9FyJJWlhG2YL4CbA1yTXA41sRVfWO3qqSJI3dKAGxqb0kSUvIKKe5bkzydOCoqrptHmqSJC0Acx6DSPJbwFbgyja+NolbFJK0yI1ykPqDwPHAQwBVtZURngGRZFWS65JsS3JLkne29sOTXJ3k9vZ+WGtPkguSbE9yU7s4T5I0JqMExK6qeni3tlHuxbQLeG9VvYjBVdhnJzkWOAe4pqrWANe0cYDXA2vaaz1w4QifIUnqySgBcXOSNwMHJFmT5M+B/znXTFW1o6q+04Z/CGwDVgCnAhtbt40MrrOgtX+6PeL0W8CyJMuf3NeRJO0rowTEHwEvZnCK6+eBR4B3PZkPSbIaOA7YDBxZVTtgECI8cevwFcA9Q7NNtTZJ0hiMchbTTxjc0fUDT+UDkjwT+DLwrqp6JMkeu3Z9fMfy1tPuJnvUUUf9wgySpH1jlHsxXUfHH+qqOmmEeQ9iEA6fraqvtOb7kiyvqh1tF9LO1j4FrBqafSVwb8fnbgA2AExOTvpcCknqySgXyv3HoeFDgH/D4AD0rDLYVLgI2FZVHxuatAk4C/hwe79sqP3tSS4BXgE8PLMrSpI0/0bZxXTjbk1/P/TwoNmcCPw+8L0kW1vb+xkEw6VJ1gF3A29q064ATgG2M7i9x9tG+AxJUk9G2cV0+NDoLwEvB35lrvmq6pt0H1cAOLmjfwFnz7VcSdL8GGUX040MjkGEwa6lO4F1fRYlSRq/UXYxzXnVtCRp8RllF9PvzDZ96OwkSdIiMsoupnXArwPXtvFXA9cDDzPY9WRASNIiNEpAFHDszCmn7dqFv6gqzzKSpEVslFttrN7teoT7gGN6qkeStECMsgVxfZKrGNyHqYAzgOt6rUqSNHajnMX09iRvBH6jNW2oqq/2W5YkadxG2YIA+A7ww6r6WpJfTnJou4W3JGmRGuWRo38AfAn4q9a0AvibPouSJI3fKAepz2ZwX6VHAKrqdp54hoMkaZEaJSAerar/OzOS5EBGe+SoJGk/NkpAfD3J+4GnJ/lN4IvAf++3LEnSuI0SEOcA08D3gD9kcFvu/9xnUZKk8Zv1LKYkBwAbq+r3gE/MT0mSpIVg1i2IqnoMmEjytHmqR5K0QIxyHcRdDJ4itwn48Uzjbo8RlSQtMnvcgkjymTb4u8Dlre+hQy9J0iI22xbEy5M8j8Fzo//8yS44ycXAG4CdVfWS1vZB4A8YHPQGeH9VXdGmncvg1uKPAe+oqque7GdKkvad2QLiL4ErgaOBLUPtYXAdxPPnWPangP8GfHq39vOr6qPDDUmOZXATwBcDzwW+luSYdgxEkjQGe9zFVFUXVNWLgE9W1fOHXkdX1VzhQFV9A3hgxDpOBS6pqker6k5gO3D8iPNKknow53UQVfUf9vFnvj3JTUkuTnJYa1sB3DPUZ6q1SZLGZJQL5falC4FfBdYCO4A/be3p6Nt5O48k65NsSbJlenq6q4skaR+Y14Coqvuq6rGq+hmDC+9mdiNNAauGuq4E7t3DMjZU1WRVTU5MTPRbsCQtYfMaEO151jPeCNzchjcBZyQ5OMnRwBrghvmsTZL080Z9YNCTluTzwKuAI5JMAX8CvCrJWga7j+5icG8nquqWJJcCtwK7gLM9g0mSxqu3gKiqMzuaL5ql/3nAeX3VI0l6cub7ILUkaT9hQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjr1FhBJLk6yM8nNQ22HJ7k6ye3t/bDWniQXJNme5KYkL+urLknSaPrcgvgU8Lrd2s4BrqmqNcA1bRzg9cCa9loPXNhjXZKkEfQWEFX1DeCB3ZpPBTa24Y3AaUPtn66BbwHLkizvqzZJ0tzm+xjEkVW1A6C9P6e1rwDuGeo31dokSWOyUA5Sp6OtOjsm65NsSbJlenq657Ikaema74C4b2bXUXvf2dqngFVD/VYC93YtoKo2VNVkVU1OTEz0WqwkLWXzHRCbgLPa8FnAZUPtb21nM50APDyzK0qSNB4H9rXgJJ8HXgUckWQK+BPgw8ClSdYBdwNvat2vAE4BtgM/Ad7WV12SpNH0FhBVdeYeJp3c0beAs/uqRZL05C2Ug9SSpAXGgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUqfeHhikbp/bfPdI/d78iqN6rkSSZmdA7COj/uGXpP3FWAIiyV3AD4HHgF1VNZnkcOALwGrgLuDfVtWD46hPkjTeLYhXV9X9Q+PnANdU1YeTnNPG3zee0sbPXVGSxm0hHaQ+FdjYhjcCp42xFkla8sYVEAX8XZIbk6xvbUdW1Q6A9v6cMdUmSWJ8u5hOrKp7kzwHuDrJ90edsQXKeoCjjnL3iiT1ZSxbEFV1b3vfCXwVOB64L8lygPa+cw/zbqiqyaqanJiYmK+SJWnJmfeASPKMJIfODAP/GrgZ2ASc1bqdBVw237VJkp4wjl1MRwJfTTLz+Z+rqiuTfBu4NMk64G7gTWOoTZLUzHtAVNUdwEs72v8PcPJ81yNJ6uaV1EuA11RIeioMiDl4Cw1JS5UBsZ8zwCT1ZSFdSS1JWkAMCElSJwNCktTJgJAkdTIgJEmdDAhJUidPc9XjvKBO0jC3ICRJnQwISVInA0KS1MmAkCR18iC1njQPZktLgwGhsRslcAwbaf65i0mS1MktCPVmX96K3N1a0vxbcFsQSV6X5LYk25OcM+56JGmpWlBbEEkOAP4C+E1gCvh2kk1Vdet4K5Pmj1tLWigWVEAAxwPbq+oOgCSXAKcCBoRGMq4n7I3jj/W+/q4Gjna30AJiBXDP0PgU8Iox1SKNbCk9+nUcZ52Na6tqKX3XLgstINLRVj/XIVkPrG+jP0py21P8rCOA+5/ivEuB62d2i279vGXfLmss62dffoeeP3Ov189eftfnjdJpoQXEFLBqaHwlcO9wh6raAGzY2w9KsqWqJvd2OYuV62d2rp/ZuX5mt7+sn4V2FtO3gTVJjk7yNOAMYNOYa5KkJWlBbUFU1a4kbweuAg4ALq6qW8ZcliQtSQsqIACq6grginn4qL3eTbXIuX5m5/qZnetndvvF+klVzd1LkrTkLLRjEJKkBWJJBoS384Akq5Jcl2RbkluSvLO1H57k6iS3t/fDWnuSXNDW2U1JXjbeb9C/JAck+W6Sy9v40Uk2t3XzhXYiBUkObuPb2/TV46x7viRZluRLSb7ffkev9PczkOTd7d/VzUk+n+SQ/fH3s+QCYuh2Hq8HjgXOTHLseKsai13Ae6vqRcAJwNltPZwDXFNVa4Br2jgM1tea9loPXDj/Jc+7dwLbhsY/Apzf1s2DwLrWvg54sKpeAJzf+i0FfwZcWVUvBF7KYF0t+d9PkhXAO4DJqnoJgxNuzmB//P1U1ZJ6Aa8ErhoaPxc4d9x1jfsFXMbgHli3Actb23Lgtjb8V8CZQ/0f77cYXwyuwbkGOAm4nMFFnPcDB+7+O2Jw1t0r2/CBrV/G/R16Xj//Arhz9+/p76fgiTtCHN5+D5cDr90ffz9LbguC7tt5rBhTLQtC26Q9DtgMHFlVOwDa+3Nat6W23j4O/DHwszb+bOChqtrVxoe//+Prpk1/uPVfzJ4PTAOfbLvh/jrJM/D3Q1X9E/BR4G5gB4Pfw43sh7+fpRgQc97OYylJ8kzgy8C7quqR2bp2tC3K9ZbkDcDOqrpxuLmja40wbbE6EHgZcGFVHQf8mCd2J3VZMuuoHXc5FTgaeC7wDAa72Ha34H8/SzEg5rydx1KR5CAG4fDZqvpKa74vyfI2fTmws7UvpfV2IvDbSe4CLmGwm+njwLIkM9cODX//x9dNm/4s4IH5LHgMpoCpqtrcxr/EIDD8/cBrgDurarqq/h/wFeDX2Q9/P0sxILydB4OzSoCLgG1V9bGhSZuAs9rwWQyOTcy0v7WdjXIC8PDMroTFpqrOraqVVbWawe/j2qp6C3AdcHrrtvu6mVlnp7f+C+J/gH2pqn8G7knya63pZAa35V/yvx8Gu5ZOSPLL7d/ZzLrZ/34/4z4IMqaDSKcA/wv4AfCBcdczpnXwrxhsxt4EbG2vUxjs+7wGuL29H976h8HZXz8AvsfgDI2xf495WE+vAi5vw88HbgC2A18EDm7th7Tx7W3688dd9zytm7XAlvYb+hvgMH8/j6+bDwHfB24GPgMcvD/+frySWpLUaSnuYpIkjcCAkCR1MiAkSZ0MCElSJwNCktTJgJD2IMmP9vHyVie5uQ1PJrlgXy5f2tcW3BPlpKWgqrYwuIZAWrDcgpDmkORVSa4fevbBZ9sVsiT5cJJb2zMOPtraPpXk9KH5f2FLpC1z5jkTH0xycfuMO5K8Y76+mzQbtyCk0RwHvJjB/XP+Hjgxya3AG4EXVlUlWbYXy38h8GrgUOC2JBfW4D4+0ti4BSGN5oaqmqqqnzG4Lclq4BHgp8BfJ/kd4Cd7sfz/UVWPVtX9DG5wd+TeFiztLQNCGs2jQ8OPMXjwyy7geAZ3xD0NuLJN30X7t9V2RT3tqSx/bwuW9pYBIT1F7Vkaz6qqK4B3Mbh5HcBdwMvb8KnAQfNfnbT3/F+K9NQdClyW5BAGdyt9d2v/RGu/gcEdTX88pvqkveLdXCVJndzFJEnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSp0/8H9JJ7TNdU/QcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看餐后血清胰岛素（mm）的分布\n",
    "fig=plt.figure()\n",
    "sns.distplot(data.Insulin,kde=False)\n",
    "plt.xlabel(\"Insulin\")\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#血清胰岛素缺失值很多，后期需要补。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#血清胰岛素含量与标签的关系\n",
    "sns.violinplot(x='Outcome',hue=\"Outcome\",y='Insulin',data=data)\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('Insulin')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#血清胰岛素含量高对得病有一些关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\www_9\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#体重指数分布\n",
    "fig=plt.figure()\n",
    "sns.distplot(data.BMI,kde=False)\n",
    "plt.xlabel(\"BMI\")\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qv9RF4L87l1JUfICZM2daW6ADNAgssnHjRvJyc6lq1rX6p+g43vRuFBYU6E1jpWy2YMECvl+1ius7lNIk9tQXiurSxM/PWlby7rvvsmnTJgsrtJ8GgUXmzp2LRLvwNe1c5/v+lHZIbDxz5861uTKlIldBQQHPPzeVril+Lml9+lPC39C5nCaxQZ54/LGja4ycCzQILFBWVsZnixdTldoRXCcYKxAVRWXTzixbtoyCgjoHVyulGpAxhqeenIS/qoI7upUQdQbLhyfEGEZ1PcL2HTuZMWNGwxfpEA0CCyxevBhvVdXRKSVOxNfsPIwxfPrppzZVplTk+uijj8hesZIbOpXSPL7+G8Qn0repj/9oVcns2W+z5hyZkE6DoIEZY/jwo48wCU0JJjQ7+b7uZALJrZj7z3l601gpC+3Zs4dp016kd5qPS8/gktDxbuxcRjOPYdITj58Tqw5qEDSwnJwctm7ZcsKbxMfzZnSjuKiQb775xobqlIo8xhgm//UZJOBlVLfSU/lvWS+PC27veoQ9e/edE72INAga2DvvvIPExOFr2umU9ventAV3ErPfecfiypSKTEuXLiV7xUqu71hGU/eZXxI6Xo9UP4NaVvLOO+80+qnlNQga0JYtW/jyq6+obNYNomNO7SCJojKjBznr1rFy5UprC1Qqwni9Xl6ePo02iUEGt65/BPHp+lWncuKig7w0fXqDf7adNAgaiDGGadOnI65YvC16ndaxvvSuEJfIi9Om6b0CpRrQxx9/zN79BfyqU+kZ9RKqT1KsYXibMr759lvWrVvX8CewiQZBA1m8eDHZ//oXFS36gSvu9A6OclHR+gLycnN5//33rSlQqQjj9/t5+x9v0aWJn95p/77oTEP5RZtKEmPhzTffsOwcVtMgaAD79+9nyrNTCSZm4Gve/Yw+w5/WEX9KG17++9/Ztm1bA1eoVORZsmQJBYVFDG9X3iA3iE8kLhp+0bqcZcu+Y+vWrdadyEIaBGepqqqKCRMepryyivIOP/1hltHTJUJlu4vw4+KhhyZQUlLSsIUqFUGMMbwz+21aJwbp19S61kCNwZmVxEXDnDlzLD+XFTQIzoLf7+fRRx8lLy+Xsg6DMO4mZ/V5Jjaeso6XsHf/Ph5++OFzon+yUk5Yvnw5W7ZuY0imta2BGkkxhp+2rOCzRYvYt2+f9SdsYJYHgYhEi8j3IjIv9LqDiHwnIrki8o6INMr1GoPBIH/961/58ssvqWzzIwIpbRvkcwNJzalo/1NWr17NXyZOPGcXwlDKKsFgkL///WXS4w0XtbDvj6kr2laCCTBr1izbztlQ7GgRjAU21Hr9JDDFGNMFOAjcbkMNDSoYDPLMM8/w6aefUtWqH74WPRv08/1NO1HZdiDffP01f/7zn/H5rG/aKnWuWLhwIXl5W7i2XSkuG695NHUHubRVBQvmz2fz5s32nbgBWPrPJCKZwBXAK6HXAlwKvBvaZRZwjZU1NDS/38+kSZP45JNPqGrVD2+r8y05j695DyrbDuSrr77ij3/8o14mUuoUFBUV8cLzz3Feip+ftLB/dtBrO1SQHFc99URj+gPO6rx8FngAqBnO1xQ4ZIypud6RD7Su60ARuUtEskUku7Cw0OIyT01VVRV/+tOfWLhwIVWt++Nt3f+UppE4U77mPahs9xOWLVvGAw88QFlZmWXnUqqx8/l8PDJxIt7K8jOeXfRsJcQYbjvvCFu3befFF1+0v4AzZFkQiMhwoMAYs6L25jp2rXNlCGPMy8aYLGNMVnp6uiU1no6ysjIeHD+er7/+msq2A/G26mfLeX0Z3ajo+DNWr1nL2Hvv5dChQ7acV6nGxBjDM888w+o1axjVtYQWZzG76Nk6v5mPoW0q+OCDD3jvvfccq+N0WNkiuAi4SkS2A7OpviT0LJAiIq7QPpnAHgtraBCHDh1i7L33smrVaio6DMLXvIet5/c37UR558Fs2bqNu0ePbpS9EpSyijGGadOmsWDBAq7tUH7Gl4Te3BzPjpJodpRE8/jKZN7cHH/GNf2qczkXNPPywgvPN4q1yS0LAmPMH4wxmcaY9sANwBJjzAjgc+C60G63Ah9ZVUND2L9/P3ePHs2Wrdso7zwYf7O6VxyzWiClDWVdLmfv/iLuHj2a7du3O1KHUuEkGAwyZcoU5syZw88zK7imfcUZf9bOUhcVgSgqAlFsPBTDzlJX/QedQJTA//QsoWeqj0mTJoX9SoROjCN4ELhfRPKovmfwqgM1nJIdO3Zw992j2bu/iLIuvyCQ0sbRegJJzSntOpSDJRWMueceNm7c6Gg9SjmpqqqKRx55hLlz53JF2wpu6mLPmIFTFRcN9/Y+Qr9mXiZPnszMmTMx5tTXSLaTLUFgjFlqjBkeer7VGHOhMaazMeZ6Y0xYdofJzc1lzD33cKCkjNKuQwgktXC6JACC8WmUdB1GqY/Q5apVTpeklO0OHDjAffeOZennn/OrTmX8slN4hUCN2Gj4ba8SLm5RycyZM3nsscfCsgegjiyuQ05ODr8dO5aSqiAlXYcRjG/qdEnHMO5kSrsOo1LcjBs3ju+++87pkpSyTW5uLv/z67vI27yJMb1KuKJdZViGQA1XFNzZvYzrOpbz2Wefcd+9YykqKnK6rGNoEBzn+++/5/77f0eFiaG067CznjbCKiY2gbKuQ/HGJvPQQw/x5ZdfOl2SUpb77LPPGDP6bvwlRUw4/yADMuwfK3AmROCq9hXc06uELbmb+PWdd5CTk+N0WUdpENSSnZ3NAw8+SGW0h9LzhmLiEp0u6aRMjIfS84bg8zTlT3/6E0uXLnW6JKUsEQgEmD59Oo8++ijt4yuYmHWADsmNb+2OARle/rf/QaKrDnLv2N/yySefOF0SoEFwVHZ2Nn/4wx/wuhIpO28IJvbMu47ZyhVH2XmX409IZ+LEiXz++edOV6RUgyopKWH8+AeZPXs2l7au5MF+h0mODc+brqeiTWKAP19wkK7JlTz11FNMnTrV8TnFNAiA1atXV4dATFJ1CMR4nC7p9ETHUNblF/gTMnjkkUf46quvnK5IqQaxd+9eRt/9G1auyOa2rqWM7Fpm6/xBVkmMMfyuzxGGhAaejR//IOXl5Y7VIyfrziQi95/sYGPM5AavqA5ZWVkmOzvbks/etGkTY8feS2WUm1KLQiBu5zJiinIBCMQ3JRifRlXbgQ1+HgI+EjYvIKbyAE89+SQXXHBBw59DKZvk5uby4APjqCw9xL29DtMt1dq/mh9fmczGQz+sNd4txcdD/Y9Yek6AL/bEMWNTIp06dWLSk0/RtGnDdU4RkRXGmKz69qsvW5Pq+WrUCgoKeHD8eCpxUdrlF5a1BKLKDyABHxLw4SrZR1T5AUvOU90y+Dn+2GQe/uMfddCZarTy8vK4d+xvkcpDPHz+IctDwEk/a1XFfX2OsHP7Vsb+9h5HppE56dA5Y8xf7CrEbl6vl/F/+AOHS8oo63YFJjbB6ZIahiuOss6XEbVxHg88OJ7XXn2FxMTwvumtVG179uzhgXG/Jy5YwYT+B2nmdm7eILv0berj930O8dRqw4MPjGPKs1OJj7fvPuVJWwQi8tzJvuwq0gqvvPIKW7dsoazDIIKeVKfLaVAmLpHSjpdQULCfqVOnOl2OUqfM7/fzx4cn4C07xLi+h2wNgQq/4Ha7ue6663C73VT47R2c0DXFz5ieR8jNzeXZZ5+19dz1XRr6H+BiqieGywZWHPfVKG3cuJE5c+bgTe/WYCuLhZtgUnOqWvZj0aJFLFu2zOlylDol//znP9mydRsjzyuhdYK93UPL/cLw4cMZM2YMV1xxBeU2BwFUz1x6RdtyFi5cyNq1a207b31B0BJ4GbgcuBmIAeYaY2YZYxrfemz8MFOhxHqoajPA6XIs5W3ZB9zJTJs+nUCg8fW5VpHFGMOsGTPokeZnQLr9A8XiXYZ58+bx/PPP8/HHHxPvcqaL6lXtK0h1w6xZM20750mDwBhTbIyZboy5BBgJpAA5InKzHcVZYdOmTaxevZqK5n0gOqb+AxqzqGgqWp3Pju3bWb58udPVKHVSe/fu5dCRI/wo3ZkpIzwuQ2VlJe+99x6VlZV4HAqCuGg4v2klG9avt22SulPqkSsi/YF7gZuAT2nEl4U+/fRTJMqFr1kXp0uxhT+1PRLr4dNPP3W6FKVOateuXQC0jNfWa4v4AGXlFRw4YFEPw+OctNeQiPwFGE714vOzgT/UWmay0THG8OVXX+NtkgmuWKfLsUdUNFUp7fjuu+X4fD5iYs7xVpBqtNq2rb5fl18WfU53Fz0Vu0ujSUqIJzXVno4s9bUI/gg0AfoCTwArRWSNiKwVkTWWV9fA8vPzOVBcRCC5ldOl2CqQ3Iqqqko2bNjgdClKnVCLFi1Ib9aUZQVugo13BomzVuEXVh9w07tvP6Ki7BlGXd9ZOgCDqW4VDAeuDH3VPG9Uan4RBhKbO1yJvWq+X13IRoUzEeHWkbex+ZCLpXvinC7HMbPz4jniFW6+2b5bsfUNKNtx/DYRaQYUm3BdauckNm/ejES7CHrCc2ppq5gYDxKXyKZNm5wuRamTuuKKK/h8yRLeXv09LeMDdI+wS0RLdsfx+R43v/zl9XTv3t2289Y3oGygiCwVkfdF5HwRWQesA/aLyJB6jnWLyHIRWS0iOaH7DYhIBxH5TkRyReQdEbHtYn1ubh4BTyrIOTBr1WnyeVLZnJvrdBlKnZSIMOHhh2nROpO/rmnC+gNnvm5wY/NZfhwzNyUycOCPuOOOO2w9d32/EV8AHgfeBpYAdxhjWgCDqL5ncDJVwKXGmL5AP2CIiAwEngSmGGO6AAeB28+i/lMWDAbZvHkzfk94rTZml0B8U/J37XJ0hkOlTkVaWhrPTn2O1m3a8cyaJny+O47Gd/3h1AWC8HZePK9vTuQnP/4xEyc+QmysvZ1Z6gsClzFmoTHm/wH7jDHLAIwx9V5sNtVKQy9jQl8GuBR4N7R9FnDNGVV+mvLz86moKCeQ0MyO0x0r4D1m6DoB+wfLBBKaYYwhLy/P9nMrdbpSU1N5dupznH9BFjM2JfLKxgS852Cv0kNVwqRVTfh0p4err76av0ycaHsIQP1BUHuij4rj3qs3o0UkWkRWAQXAImALcKhWF9R8oPUJjr1LRLJFJLuwsLC+U9Vr/fr1AAQT08/6s06X+L3HDF0Xv/1BEEyo/r5r/h2UCndNmjRh0qQnueWWW/hyr5v/zU5j65Foy87XNtGPJzqIJzpItxQfbROtvT+RXRjLw9lpbC/38NBDD3Hfffc51r27vgtwfUXkCCCAJ/Sc0Gt3fR9ujAkA/UQkBfgAqOvuR52BYox5merpLcjKyjrrhuG6deuQmDiC7pSz/ajTZlyxzJs3D2MMH3/8McZl/+pnJsYDniasXbuWG264wfbzK3UmoqOjGTVqFL179+bJSU8wcYVwVbtyrmpf0eAL1Nx0Xjk7S6t/JVq5DkGZT3gzN4Gv98XRuXMnJkx4mA4dOlh2vlNRX6+hBolfY8whEVkKDARSRMQVahVkUj2hneVWrV6DLz4dR8auR8dSWX6A9957r/p1kv1hBOBLyGDNmrUYYxAn/h2UOkMDBgxgxsxZPPfcc3y4aBEri93c3vVIo1u3eEVhDLNykznijeKWW27i5ptvDotBnpZ1nxGR9FBLABHxAJdRPUL5c+C60G63Ah9ZVUONkpIS8nftjLjxA8cLJDanpOTI0aH8SjUmSUlJTJgwgUceeYQyVxp/XpHC7Lx4qhpBFhyqEp5fm8jUtcmktWzPiy++yKhRo8IiBKD+S0NnoyUwS0SiqQ6cOcaYeSKyHpgtIo8C3wOvWlgDUHsgmf33B8JJIHSfYOPGjUeH8yvV2Pz0pz+lX79+TJ8+nY8//pjsIjejzjtCj7TwG3NgDHy5N463tyTixcWdd97Gr371K1yu8OoWa1k1xpg1wPl1bN8KXGjVeeuydetWoLoLZSQLeppAVPTRfw+lGqukpCTGjRvHZZddxjNPP8WkVcLPWlZyQ+dyEmLCo69pQUUUMzYlknMghj69e/H7cQ+E7R9g4RVLFtm9ezcS4wZX5A5bB6oH0rmTyc/Pd7oSpRrE+eefz2szZjJz5kzeeee5HHMWAAASoUlEQVQd1hysbh30beZzrKagqR4h/M6WRKJj3dx332+48sorbZs36EyEb2UNqLS0FBNTbyeniBCIjqWsrMzpMpRqMHFxcfz6179m+vTppLRox1/XJPPKhgRHVhgrrIjiqVVNeH1zIn36ZzFz1utcffXVYR0CECEtAl2d6wcGwe8Pv2upSp2t8847j5de/juvv/46/3jrLTYciuPuHkfo1MSen/dl+2OZsTkJcbn5/e9DY4YaSe+88I6pBtKiRQukspRzepz6KYrxltKyZUuny1DKErGxsdxxxx08/8ILRCc359GVTZi3w9pprasC8OqGBF7MSaJDl+68+toMhg8f3mhCACIkCLp06YIJ+oku3e90KY6SisOYqlI6d+7sdClKWapnz578/ZVXuXjQIOZsSeD5tUlUWtAwKKqM4tGVqfzfPjcjRozgueeeb5R/aEVEEFx88cV4PPHE7I/s6RViC9YTFR3NZZdd5nQpSlkuKSmJP//5L4wZM4bvi+N4ZGUqhRUN9ysv97CLP69IpdAfzxNPTOLOO+8Mu26hpyoigsDj8XDddf9FzMHtuA5ud7ocR0SX7CO2YAPDhg4lLS3N6XKUsoWIcN111/HU009z0CQycWUq+aVnP2HC90UxTPq+CUlNWzJt+ksMHDiwAap1TkQEAcAtt9xC5y5diN/+NVFlRU6XYyupOEz81qW0bNmKu+++2+lylLJdVlYWf3txGq6EVB5flXJWk9ct2x/Lc2uT6di5Cy9Om067du0asFJnREwQxMTE8MjEiaSnpZC4eT7RJftsO3cwPg0THYOJjsGf1IJgvH1/kUeVHyBp8ycke2J57LFHiY+3f8I7pcJBu3bteP6Fv5GYmsFTq1POqGWwojCGaeuT6NmrF5OnPEuTJufGaocREwQALVu25IUXnqdl83TiN88nZl+OLT2JqtoOJBDflEB8Uyq6DaOqrT3NSFdRLokb55GaFM8Lzz9Hx44dbTmvUuGqVatWPDv1OTxJqTy9JoXiylP/FbjpkIsX1yfTrWtXnnzqaRISEiys1F4RFQQAGRkZTJ82jZ8MHIh713d48j5DfOfYql3+Ktxbv8Cz7Uv69OrJS9Onh+3QdqXs1qJFC55+5q94ozw8u7bJKS14U1wZxdR1TWjeohVPTHoSj8djfaE2irggAEhOTuaxxx5jzJgxuEv3kbTuA2IKNjb+cQbG4CreSnLO+8Qd3MbIkSOZPHky6emRPdmeUsfr2LEjf/zfP7GjJIq3ck/+l70/CH/LSSYY7eaJSU+SkuLMNPJWisgggB96E8yY8Rp9enbDveMbEjZ93GhvJEdVHCI+dyGerUvp3L4NL730EiNHjiQ62roVnZRqzAYOHMh///d/8/keNysLTzwd9D93eMg7HM24Bx6kTZs2NlZon4gNghpt2rTh2Wef5cEHHyRFqkhYP5e4bV8hvuNX5gxTfi9xO78jIedDEr0HGTNmDNOnTaNLly5OV6ZU2Bs1ahQd27fn9dxkKvzVy1XWXqJyT1kU/9wRz+DBg7nkkkscrNRajXP0QwMTEYYOHcrFF1/MG2+8wbvvvkfcoe1UtOiLr3kPiArDv6pNkJjCzXj2fo/xVXLFsGHccccdpKamOl2ZUo2Gy+Xid+PGMWbMaObt8HDTecfeL/xHXiJuTzyjR492qEJ7RHyLoLakpCTuvvtuZsx4jQsv6I87/18k5XyA68D2sLp/EH14N4nrP8K94xt6devCyy+9xLhx4zQElDoDPXv25JJLLmXh7gSOeH+YHyj3sIs1xTGMuOnmc34QppVLVbYRkc9FZIOI5IjI2ND2NBFZJCK5ocew++3Vrl07nnxyEk8//TRtm6fi2bKE+M0LiKo46GhdUnkET95nxG9eQIukOCZOnMhzU6dy3nnnOVqXUo3drbfeijdg+Cz/h+nq5+3wkJKcxDXXXONgZfawskXgB35njOlO9aL1o0WkBzAeWGyM6QIsDr0OSwMGDOC1V19l7NixJAeOkJDzIXE7l0HAa28hQT+xu1eSlPMBCeUF3HnnnbzxxusMGjSoUc1wqFS4ateuHQMGDOD/9sUTNHCgKopVxbEMv+rqc66raF0sCwJjzF5jzMrQ8xKqF65vDVwNzArtNgsI67h1uVxce+21vPXWm1x15ZXEFmyw9XJR9OHdJOV8SNyeVVx6yX/w5ptvMGLECGJjYy0/t1KRZNiwKzhQCRsOuvh2XyzGwNChQ50uyxa23CwWkfZUr1/8HdDcGLMXqsNCRDLsqOFspaSkcP/99zNkyBCefuYZtm1Zgj+1LZXtLsLEWPAXg9+Le+cyYorzaNmqNb//3Z+44IILGv48SikAfvSjHxHjimZ1cSw7Slx07NCe1q1bO12WLSy/WSwiicB7wL3GmCOncdxdIpItItmFhYXWFXiaevTowd9ffplf//rXxJXsIWn9hw0+o2n0kerPjTu4lZtuuomZM17TEFDKYh6Ph959+rDmQBybj8Qw4MIfOV2SbSwNAhGJoToE3jLGvB/avF9EWobebwkU1HWsMeZlY0yWMSYr3EbGulwubrzxRl75+9/p1DYTT94S4nZ8C8GzXBLTBInNX0H8pvm0Tk/hb3/7G3fccQdxcXENU7hS6qS6d+/BnrIoAkHo3r270+XYxspeQwK8Cmwwxkyu9dZc4NbQ81uBj6yqwWodOnRg+vRp/PKXvyS2YAMJmz5BvGe4MLy/kvjNC4nbu5qhQ4fy6iuvRNQPolLhoPbqfZ06dXKwEntZeY/gIuBmYK2IrAptewiYBMwRkduBncD1FtZgOZfLxd13302vXr14/IkniNo4j7LOPz+tqaal8giJeYtw+cq5/4EHGDZsmIUVK6VOpEWLFkefN2/e3MFK7GVZEBhjvgJO1LdxsFXndcqgQYNo3bo14x54ANn0CWWdLyOQ1KLe46LKD5C4eQHxcS4mPTWZ3r1721CtUqouGRk/9F2JpJ55OrK4AXXq1Inp06bRqkUGCXmfHTOBXTA+7d9aCVJxmMTcBaQmxzPtxb9pCCjlsKSkJKdLcIQGQQPLyMhgyuTJpDdNIzF3IVJVAlQvTlN7QRrxVZKYu4AkTxzPTpmi6wUoFQYiqRVQmwaBBTIyMpj812dwu6KI3/I5BP3H7mCCeLYuxRWo4qknJ2kIKKUcpUFgkczMTCZMeIiosiJi96455r2Ywk1EH9nDvfeOpVu3bg5VqJRS1TQILHTxxRdz6aWX4t639uglIvFV4tm9kvPP788VV1zhcIVKKaVBYLnf/OY3REUJsfvWARBTsAHjr+Kee8bohHFKqbCgQWCx9PR0Lhs8mLjiPPBXEVe0iQsvvJCOHTs6XZpSSgEaBLYYOnQoJuAjbu9q8JYzZMgQp0tSSqmjdKlKG/Tq1Qu3Jx72rSMqKooBAwY4XZJSSh2lLQIbuFwuunWtXkUss02biB20opQKTxoENqm5J9BJ7w0opcKMBoFNaqbSTkxMdLgSpZQ6lgaBTdxud/07KaWUAzQIlFIqwmkQ2EwHkSmlwo0GgU00AJRS4UqDwGYaCEqpcGPlmsWviUiBiKyrtS1NRBaJSG7oMdWq84crY4zTJSil1DGsbBHMBI6fS2E8sNgY0wVYHHqtlFLKQZYFgTHm/4ADx22+GpgVej4LuMaq84crvTSklAo3dt8jaG6M2QsQesyoZ/9zjl4aUkqFm7C9WSwid4lItohkFxYWOl3OWdOWgFIqXNkdBPtFpCVA6LHgRDsaY142xmQZY7JqpmdQSinV8OwOgrnAraHntwIf2Xx+pZRSx7Gy++jbwLdAVxHJF5HbgUnAz0UkF/h56LVSSikHWbYwjTHmxhO8Ndiqc4YzvUmslApXYXuz+FylN42VUuFGg8Bm2jJQSoUbDQKbaYtAKRVuNAhsUhMA2iJQSoUbDQKbaAAopcKVBoFSSkU4DQKllIpwGgQ205vFSqlwo0Fgk5p7BMFg0OFKlFLqWBoENikrK3O6BKWUqpMGgU2KiooAOHz4sMOVKKVOJFJ792kQ2GT37t2hxz0OV6KUOhGv1+t0CY7QILDJ1m3bAdi5ayeBQMDZYpRSdaqoqHC6BEdoENigpKSE4qJCAu4m+Lzeo60DpVR4idR7eRoENti0aRMAvvSux7xWSoWX0tLSo88jqeWuQWCDDRs2AOBr1hmJdh19rZQKL7VbBOXl5Q5WYi8NAhts2LABPCngcuOPb8b69eudLkkpVYfaQVC7dXCucyQIRGSIiGwSkTwRGe9EDXbatDkXnycNgEB8U7Zs3aoDy5QKQ9oisImIRAN/A4YCPYAbRaSH3XXYpby8nOKiQoKeVACCnlR8Xi979+51uDKl1PEqKyvrfH6uc6JFcCGQZ4zZaozxArOBqx2owxaFhYUABOMSj3ms2a6UCh9VVVVHn2sQWKs1sKvW6/zQtnNSzUhi43KHHuMAOHLkiGM1KaXqVrunUCRdvnUiCOqafvPfxnWLyF0iki0i2Y35r+cTzTaqs5AqFX5qTzGhQWCtfKBNrdeZwL/Nu2CMedkYk2WMyUpPT7etuIYWF1fdApCgr/ox4AcgNjbWsZqUUnWLjo4++tzlcjlYib2cCIJ/AV1EpIOIxAI3AHMdqMMWNSEm3rJjHhtzuCl1rqr9yz8mJsbBSuxle+QZY/wiMgZYAEQDrxljcuyuwy4pKSl44uPxVlTfK4iqPISI0KpVK4crU0odz+Px1Pn8XOdI28cY8wnwiRPntpuI0LlTJ1Zv208VEFV+gFatW+N2u50uTSl1nEgNAh1ZbIPu3bsTXX4AggFiy4vo0b270yUppeqQkJBQ5/NznQaBDbp3744J+nEdzsd4y+muQaBUWEpMTDz6XINANahu3boBEFOw8ZjXSqnwUjsIIqlnnwaBDVq0aEF8QgKuI7sRETp16uR0SUqpOkRSK6A2DQIbiAjt27cHoHmLlkfHFiilwkt8fLzTJThCg8AmbTIzAWjXtk09eyqlnBKpvfk0CGzSunX1dEo6fkCp8BUVFZm/EiNnDLXDrr32Wlq3bk3//v2dLkUpdRIJ8fF07tLF6TJsJbUnWQpXWVlZJjs72+kylFIRoKSkhNjY2HPiXp6IrDDGZNW3n7YIlFKqlqSkJKdLsF1kXhBTSil1lAaBUkpFOA0CpZSKcBoESikV4TQIlFIqwmkQKKVUhNMgUEqpCNcoBpSJSCGww+k6ziHNgCKni1CqDvqz2bDaGWPqXSC9UQSBalgikn0qow2Vspv+bDpDLw0ppVSE0yBQSqkIp0EQmV52ugClTkB/Nh2g9wiUUirCaYtAKaUinAZBBBGRISKySUTyRGS80/UoVUNEXhORAhFZ53QtkUiDIEKISDTwN2Ao0AO4UUR6OFuVUkfNBIY4XUSk0iCIHBcCecaYrcYYLzAbuNrhmpQCwBjzf8ABp+uIVBoEkaM1sKvW6/zQNqVUhNMgiBxSxzbtMqaU0iCIIPlAm1qvM4E9DtWilAojGgSR419AFxHpICKxwA3AXIdrUkqFAQ2CCGGM8QNjgAXABmCOMSbH2aqUqiYibwPfAl1FJF9Ebne6pkiiI4uVUirCaYtAKaUinAaBUkpFOA0CpZSKcBoESikV4TQIlFIqwmkQqIghIgERWSUiOSKyWkTuF5Go0HtZIvJcPcePFJEXTvOcD51NzUrZQbuPqoghIqXGmMTQ8wzgH8DXxpg/neLxI4EsY8yYMzmnUuFKWwQqIhljCoC7gDFS7T9EZB6AiFwoIt+IyPehx661Dm0jIvND6zocDRARuUlElodaHC+JSLSITAI8oW1vnWS/aBGZKSLrRGStiNxn57+FUi6nC1DKKcaYraFLQxnHvbURGGSM8YvIZcDjwH+F3rsQ6AWUA/8SkY+BMuBXwEXGGJ+IvAiMMMaMF5Exxph+ACLSva79gBygtTGmV2i/FCu/b6WOp0GgIl1ds7I2AWaJSBeqZ2iNqfXeImNMMYCIvA9cDPiBC6gOBgAPUFDH5w4+wX7/BDqKyPPAx8DCs/+2lDp1GgQqYolIRyBA9S/j7rXeegT43BhzrYi0B5bWeu/4m2qG6jCZZYz5Q32nPNF+ItIXuBwYDfwSGHXK34hSZ0nvEaiIJCLpwHTgBfPvPSaaALtDz0ce997PRSRNRDzANcDXwGLgutANaELvtwvt7xORmhZFnfuJSDMgyhjzHvBHoH+DfaNKnQJtEahI4hGRVVRf6vEDbwCT69jvKaovDd0PLDnuva9Cx3UG/mGMyQYQkYeBhaF7Dj6q/7LfAbwMrBGRlcaYESfYrwKYUdOVFaivZaFUg9Luo0opFeH00pBSSkU4DQKllIpwGgRKKRXhNAiUUirCaRAopVSE0yBQSqkIp0GglFIRToNAKaUi3P8HVrEd7uvAL+0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#体重指数与标签的关系\n",
    "sns.violinplot(x='Outcome',hue=\"Outcome\",y='BMI',data=data)\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('BMI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d7937c8390>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiahZ1MzvSzH5jZkvNbLGZXdFIw4QQQgydsXXc2wVcGUJ42Mz2AR4ys1+GEJY0yDYhhBBDpOaeeghhTQjh4fh9M7AUOKJRhgkhhBg6DRlTN7MpwKuBPzQiPCGEELVRt6ib2d7A7cDfhBA29XF9jpnNN7P53W0bBwxroPOLp1z1i+J87irORs/99Ol/kDOZe52TPVhYVYbTl3snW4ZgV5/k563Xcx70EM8hr4dB86TS/3Cfc11lGQylLPr7bcDz2Os4O3+ocQ94bYh+h3ze+WDhDcKg9SH/3wj1/H+Dvu4d6Bz/ETr3vZK6RN3MxuGCfksI4Sd9+Qkh3BBCmBlCmNk0YdckUgghdhfqWf1iwHeBpSGELzfOJCGEELVST0/9TOAS4M1mtiB+zmmQXUIIIWqg5iWNIYTfAdZAW4QQQtSJdpQKIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJ2GWiPtjxuJXXBwtryHEPdLxnHUdm9mXLoEcFD3T86hDCHjRdg9ArrDrCqSqeQY723eno10GOZe732iDp6DNP68nDQcpyqPb0dwQ0MGAeVnXkc5Xx9GXTQH6mtN+6U3j9+R8sTZVH2A6aB+237nRkcb9pv2bioHYOdFTvQOncKd7Ko3gHcfd5THCVqKcuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAloi5RN7OzzexxM3vSzK5qlFFCCCFqo2ZRN7Mm4BvAnwGvBC4ys1c2yjAhhBBDp56e+p8AT4YQng4hdABzgfMbY5YQQohasBBCbTeaXQCcHUL4YHRfArwuhHB5hb85wJzoPB5ozS6vBw6s0V3PvYpLcSkuxTWa4torhHAQVTC2Gk/9YH38ttMTIoRwA3DDjpvM5mfXZtbqrudexaW4FJfiGk1xhRCmUCX1DL+0AEdm7snA6jrCE0IIUSf1iPofgWPNbKqZjQcuBH7WGLOEEELUQs3DLyGELjO7HPhPoAm4MYSwuIpbb2igu5FhKS7FpbgU18s9rkGpeaJUCCHEyw/tKBVCiBIhURdCiBIhURdCiBIhURdCiBJRz+ajqjGzSUAIIWyowu9E4GzgCHwz077AIuAjuL0GPA0sAR4KIfzKzN4DnAEsBR4GukIIf4xn0ZwNvC2EMKuP8MfjK3d+FEJYbmZ/BUwDjgGuBL4ENANrgcPiPWuA32b3vAq4AH9ATgIOivesAiYArwI2Aw9GP/eFEH4UbXsbMBu4IqbjSuB/ANtjmqnwf3a858/wJaSr8f0Cn8XP33kRuAd4CnimIg/OBb4HrI5xXQa8C9gKPBTzdbB82ILvTTgEaIv58I1479nAk8B+0a5fA38HnB5t/HfgdyGEHjM7FZgCLAQuiX73wY+ZmBbzbjnwO+APwKHAF/G9EPsBLwH3As8C92Y2vy7m+WPA/sBewOExH58H7gB+E/P3z4F2/LiLJmBddJ8R7f9jLPcHos2vBo4DTgD+Bbg8hr855lNudzV1A7xutAL3RT/DVQ87gJNiun4MvDCM6crjuiP+3RxCWBjL/R14fUnhHwtsAn4B/BD4U7zOTI+/T8J3Vn4hls9fA/PxuvsEXo82x3iPAB4F/jGE0B3TZsB5Ma/ejevJQ8DXgc6Yn5/G69aeMZ1/H8O9Aq9P/wG8NX5/GK9LJwB7A1fjbeu8mK+rgKNjnq6J+X10tP0tMb4OvI4uiGn8E7z9tsUw9ov33gdcGEJ4H0MhhDAsH+Ao/DyYdbjIPItXynRGzE3AjJiYj+MN+33R70LggZiwDlzcu4AevBFsi5m0NBbQY7gIboj+NuIFvhUXgG68YmyNBflCLJz2GGaI10LmTt/zz4aK6ym8jop7Ai4cAa/UG6K7I9qXbNkU/bdl6XsRrxjpntz/2piWLdH2bTH+VryCPBu/d8VreR70ZPekPGnJvleTD93xsyn7vjHa9yW8cW2OYXTFdCWbU1ir+siryjwN2efhmN7cvp6Ke5ZU3JOub83KIbfhUxX+8/LaVpEflTaHiuuV+VZN3dgW/66P33v6CKdR9bAt/t0Uyz+3sdHpasv8pN+64t91faSpMp3bM//JnrzcOirC7I7fW+P3zszvCor2l+LprojzFxXxpU9nRbwpXzb2ca2jj/sr61vl79vjJ7enK4b1fPy9Pebndnz/z8+AD1SlvcMo6g/gT8Z3441yXcz8FRSVsysavzy6O7NM3h7d23HRzoU9fVIl+kV0pwzuxhtL8p9XpO14o0gVZjveuJKf57Lv7VkYqTJvprfIdMQwkvtFXExDTO+DwN9k6d2SpeXpChufx0U22ZPSESruDbGCpTBbgf8X/6ZKmWzOw0/pT7bltqdKO1g+tMa/P83C7Y5252WUHixtWVl0Z+Ftz8LvytLXGf9uzfwviH83x7//yc7Cmz94Oukt0qkn183OwrW+wu9zFA+idD35XVNhcwe960ayu5q60YHXw1SOeVyNrofteN16YQTS1R7vuTUr8yS+Kb4U/nb8YbOwwob7srLM23K6/8PZ9/Qg6Irxdlb4XUdRR7fg9TTlQ6pTSUSTYKf6mx6ggaLtpetb2Fn4U7tLD5dn6V3X/jZLV8qH9uhuzeJ9EpiFP3Q7gTfGz4pqtHc4x9QPDCH8B/5qswlviP+Gv36kBDXFzPlwvGcs/kqXnmJN0X1IvN5E7x7vnvH3s6O7KV4z/HWwiULktuMFOD7G0wOsjJ/fxXBCDDMVwrP4Qwj81WssXvnWx/uI9qXCBReEk+P3fYGZwJejOwldSksbxbxGT7SvGTgghrccGJf5Tw0nr1xN+NDFmXjDTbZbTEueBx1Z/PtSCHEn8P4sDX3lw7Px2v7x7ztjeMn2JyrubwL2oBjaWB/zjpimXNRXx7D2iv5X4w0vhZ/KeUL0n16dn8PrVrKBmC9bM3s78DIfR/F2ks4tCjGetGluD/wVfmL0+0IWjtFbLB7H8zvZldvdEn8fqG6MjenaK0vLcNXD8Xi5HTAC6UpDmm+ld6ejKV7fnoU/PqY/+UnhvZbiAdOJD0m2ZfamNtFBIcRjYr58Kv6W/BLDGoM/QF+ieEiCt7eU9iW4iBtFfUpvRUm0U9h7UDx4ky2Px+8prcvp3Uu/i6KzmsJrivGdFK81AVOB7+Dl1YTr5tcpdHBghrGnPhe4Hn/qHAt8Gxet9FRNlSLviXfghZcS30bx1Esi1kbRa8hfw5JIfA1YFv3lPYtWfOw5hZF6AS/iQz55ry/Zlb/+pQfR9hh+RxZW5etVnr40JNSOP8CepXgl3JDd9xMKgQ0x3PtifMl/ZQ8q73EEigq7HW/sD2bpSm8nnTHN+evoFnznWt776i8fkh1p/DTl/fNZuaQ05/7voRguqnx1X08xNJDqwNPx+4uZ7emeF2J5vpjdU9nrz3tIayka05MV1/LeWQ/eUNsoho4eya7n6enMbO7uw+6B6kYKay3Fa/Zw1cMU3oqY/1tiWQ1nurbjgpYPieRxVYYf2Dnd+ZvXSlyQb4jXtlX4S291W/F6dlNFGH21y84s/J4+wk2f1J6Sn/X0fkPKh3y6s2tp2DSFmY8ipLK/lWJY6tf0fnvaCsyL970CH+NfXY32DudE6fuAy4CLgDtj4veIifoW8Jf4k3oDxVN4Mz4BMw2fVFiLF+TV+Cv4/hSTZjfjk4x/FzNiD+CLIYRPmdkK/OnXjT+9u/Fex2djPBuAX8X7XgecEULYYGYG/CvwJnyCoxOfGBwbv0/HC/hm4FchhBcAzGwcPvzRjE+qHBnjnBHT8Dr8AXc/8F94492K97DBC/bPYxxj8Qb+RXzS5zP4/MRWvFcDLjzHx/xIr6CdwPdjuv423nNglgdj8J7E8fhEzLqYZ8vweYmFwEkhhDMGyIdufFJocQzrPTHuI4GDKXo4e8W83Rjz+nl8cmsS3us6P+bDd/BJtrvxunA83gtri2l5IITwmJn9JT55dnjMh2UUvcqfx7Ibh487NlE8OA/BezuGj83fjjeSPYBPUkxanYALx5pow+ZYbg/hArE1lu0NeI/q7lgOR+GNd1P83stuGLBuLAZej78VrAR+n6Wl0fWwFa+DB+AP/vuBHw1TulqBc2K+PhDzsTOW1WS8fW+h6CkfENN/Bz4pvnfMk7+I/g/FJ7c/F0J40cxuxbXhL/BOTwf+BjERf8u6C+9QXovX+avwtrUiurvi96/iD6p3xXI4Kdrx/WjLFor69Lv493C8XR6CT6hPAP4hXvtMjL8Zb5/TY3h/C3wl+n8c75DMxDu6U/D2cXW857XAJ/B6Oiem8akQwnMxz++lCob9mAAzOwXYGkJ40sy+i6+CeCWeyP+OZ96heGZsxxt3euq/Dm+ct4cQ3mBmk4GjQgj/lYU/HR96GBNC+FYf8V+Ij7mfBZwIPB5C+PFwpbcvot1dIYTn+7h2Fl7xj8NFeDle+Dv5N7Pj8Kf2WryBT8IrxzxiQ+knjpQH0/BGMxlvgItCCMvqTUu0aw0+JLMot2uo4QuxOxB160RqaIODhj3cot5npGbnhhDurPhtH1x4j8F7SE+HEJ7Jru842CaEMCe6Dw8hnFsRzp2Vv6Xf4707XcvDz8KujKuR7l429mVb7qca/5X39ZUP/d1XEVct+XB4f7b2Ydc1IYRrzOyaLKyB3IcPFHcd5bG6irh7uRtp92DuEaqHI5muXZFvq4eYttx9+BDjGsy9eqCwc10wsxuSO17rsz33x4isU++D1wJ35gkKIWw2s0uj+4t93PO/+3CvzkUs/v4hKAQshndu/nuF/3Q9D7+vuBrp/lCFHR9KF/pJRzX+d6Qz91Mh5P3lQe43DVENJV2rc1sHsSvd81BFWP25f16FLbWUx+FVxN2Xu5F2D+Ye7no4kunaFfmWyngo5ZvcP8/cjSij3Jb+wv5QRRv8UOW1aoR9WHvqZnYCPn6aNhKtBn4WQlgar78GIITwUC4IIYQ11T6lzOyw6P+wdG/6PflJv/Xlv/J6lek6OISw1swOzsIY1N2f3ZX2ZXlwADA+s7cDOCSEsKTSP7B/+j0PszJfBsuzoeZFX2mqsKth4QtRdgZqI9W2n2ETdTP7FD5JOpdiKdRkfCfk3BDCdRX+D8MnZK7Bl9ftR+9/mZdm1VfgExnXAbeFEP4sC2NffEK1DZ+A+GYI4dZ47Xp8mZjhExWP4hOZJ+ATKFvw8fw9Yly5uwtf7TERf7sZiy/DzNPwMXxCJPFNfDI4xDifxCdG7sd3rP09PoHThU8ErcXHu2fgD7/T471bgc/FfEn/o3A7PpHyKfzJ/xaKCcBtMZy5wDdCCE/F9D+Mr7C5DZ9g/RZeHkvwN6e9Y9hpeZj14w4V3zvwicWn8EnFI/DJ0GkUyymNYulWf6SVAe14GV+Pr5j6RnwV/TZeVq/A69MBMa2r8OV0+Yau5N4W8+pofMnlYRQ7B5+Lad4/xrsqxn0IxSTZuUAIIRxnZst3GFq4x8f0XxD9T8Qn1Q6P9k3EJw7TzsjkNnxJacrXsRSTy/nEdlpFkdxpZURaMteC16u94/XrgbfH+JP7D3jP71h8MvatFP+xrA2vM+Mo6lpyp9Uie0Qb0wqPVFaVbbOvsq2lvK/H6/Rn8PJLZfwavF2kMj4sfj+EYn/FZryNTKNY275/9PcwPpm/F142R1Ls2u7AdenXFCu3tuBltjfFKrx9Y1zjKHbRphU047J8SpPAKb/STnijdx6mSd7tePs/Nf6+J15+m/Dy/VYI4XsD5GVFzg7fksa0xnomvvLheVxU7mfnZUOduCg8Fu97CvhHXOy+jU+utuCF/xC+FGghvoLj98APKJb5deMbY9Ja1K24cKW17Vvi9TQZmy/hSitJKneeNerTjU8odsV42vElmGlTTSe+/T0toUprivPlUtcOEH5aPpU2RqSNJJvi59vRnZZ4pvXeXfiKm3z3YNqUkdytWZgpXwfKp3xJWlo6+gLFrtb0aY+/r8cb78MxD1rwrd0v4Fv2g83PAAANNUlEQVSw8w1M+ZK6yh2gle6tmTvf+JLb1+hyTuW1nWJzUV/uZzI7tuKrRfJy35S5N9F7+VxlXHk55O6emCcPUrSBtCEs3Z/X+UCxHDg9VLZm+ZbqS1rOWunuyuJoi3YPpbzbKDbd5GWaL0/Ml/L2ZPnUQ7GZp572m+pEKqPkTqt4kp9NFEsSUxmGCnfSnB6K3aJpWXZn5ve3Wfyt0b0Ufxh/H7i2au0dRlFfhveqHowZcR2FMOTimRpaZcamytATw0gVIq1HzxtmqnDp3rUxI/O1oXkcd2T3ttF7l+sj9G74n8gKuJ2iIa6l97reL1W4l0Rbk/+0+WZuZlMLvXedraVYQ94Tv6dK0x3zMa/AT9O7AeZp3Ar8n4p8SfEm4Uv+u2K6B8qHRzN3B/5GtC37bQWFaKT14EkgnsbFLO1ifRx/eKcHTGX5DPbJxa0DX6aXHtL9uZP/RyvuX0Dvrd8/jPmejpDYgi+9TLbm7m68x/8IRZ1Ob4opD3NBqnSPy7634+fNJPfjFWX7SOZOD+pciFM5dvfjzoXuyUHKemHm3obXpUey620Uy0r7cm+L7u3xU295J7tTmT5L77XxqZMWKLbyp/v+jd7t9wmKTkay45mKMlswQBnl1/oro7xddeP1vwfvgKa214W/4aX2mW/KTPtoAr5c9Ahg2ctB1NMBT5twQbshM/Q5/LyXVMG3UAh+6tFswZ/YqUdRuTFgoKdwKsQkOvmTtwdfP5qevG3A5yl2iz1Ksba2M6YlbY5pxdfBpt7NpqwQn6hwp6dw2imXxCW9Kvbgop4qYCfFWSWpMjybFXTqkaRefohxpp78dnyYJm/s6W3ghRhvZS81nQmT7MvPBtlIb4HYSNFwOvCHVhK+NEST8jcvzx68QafNStvwh9sait7bBrxhJRvWRnd62zoqK59UJvmuvHwjSe5OPciUx8m2lJYuvJG+GD8bcWFNr8OpjiyPNrZXuNP1TRTCuT7Lp5foLTDJnfLlxxRvEl0xT1L5b6boxKR0rKcQpxb8zTYdg9AS7Wrvw72VYkNOyPwk9/OZzZXuJDKpzaQe6IoB3Ml/OrtkKOXdiT8QVmZluY5CwLspznVJZZzHnToW3fjSWyiEfi3FgzN/O0lnL6Uyyo9r+F9Z2aaHaSqzdfQ+MiFpXap/+cbDZHfSozTUV9mxbccfqudTnG/1fLRxTjXaO2zHBIQQ7sbXXj+HF+QkiqcXFGPAAR+S2YveOybTCXvN0X/y24lvsDkNz6QuvBK04BX1foqdhs34ONYqCjFtp9iaPyaG98No51P4K/CC5DazGXhF+E2M51AKYd0THy9LhZy7X4px7xfj6sIrwfiYRmK+JFvW4oU5PtoJvjkpUIxHp7HTtJvyaIpt+2PxzRDp6INH8DH7jnjtrmjf0/js+7LoP70NjKPYHBYoxnxzd1MW14lZmbTibzTd8XozPn5MtPsYvCzHxWuHUuxNaI55NCWGm7a0r8CHmn4d3Qui7U+b2dnR/WT8jMniyt1j8LLLxzIfjeneEO1+Cn94Po2L5EX4Q3Df6L+N4igGq3Cnhp/qaNrCnhpxGkvtrnCneYjXUYyfNsU8Sdvs9475m9zj8LHgMfH3g/Hdkw/jZf10COE4/K02d78NL+P3xHRtj/enOZSAi0tTP+5x0cbJWR42U4zLV7pTOtPcxXiGVt6P4+315xRlvhDf6JTy+PbMljG48Cb+mPlrNbOT8c7PozHPoXgApHqxR5b2dLxFKrMLKQQ9tddUZgfG/Bkfw9oHL6N0DMl+WXhjYp6k+ZG0qWkN3hb/GOMZj88dfQa4K4RwInAKPgRzOlUwUpuPvomPDY3HG84ZFJMo24EP4EZvxJ92Pwf+Cn/qvR1v2DfjjWVffLfdnwL/DReLdfgOvYV4Rr8Tz8jX4QX1f/EK0gR8EK8U76co3M/iFbkVF+RTY9x9uTvww3WejunZFtPWl/tNuLgfH39fHdNwOD409VT0m15vN+C78brwgp8Y09GG97r2wV/FJlCIRTvFWSr34xX423gv8MqYt10hhAvN7IP40aVn4o3wNPxhMjXaMD7mQxpff3VMd+5uwvcTHErRSA6lmMhbgz8oD433/QZ/QM6iOFHyFTG8RdFfcj+GP/yTPf+FN4QT8Ia+DJ9I3qviWlsN7gdiXvblXoV3Ek6OeTsfF6FpeP3sz30UXoeHYssqXMhOjukayK6+3LXElefhkXj9bOvHfRTeOWvD69eRWdh9uZsoJirvw+dDUi/0aophyuS+ltjTDSG8z8xuJjIU92B+8fb+fVyQ34zvIE1n/oToXoRv0qs2LovuS8zsB5nf5E4PyPdS7ExOR2q8CR+KzR8uJ0Q7v43Xu8fjtUUhhHuokhHdfBR7vd/BK8kivDFcgDfslLC815V6tD30Fo3f4lto01kit0T/R+O7VNtxgUrXHsCHJibgmdVEceBXN0WvxOI9aTVAI9yP4jto07EA7XjFSjPyJ0X/e+EPgFV4Q0o0x7SkV2jDe5P74D3MY/He6QExre+J4e1D8ap3Sww/5VlKbxPFap50REE1+RBwwR4f0/ISvprnPIrT5V5B0bNpzsovhc8Q3fXcq7hGJi5RHWlF0/O49nVUXE9zD/vgnYv9iEMyIYSNg4Y+XGPqVY67ryAeJ4n3KFNiFuOvH2k8tPII2TTGlT6V4+s30nvMrHJC6d0U48E9eG8hH5N7vkHuNGmYj+k+RzEZmn7Lx/bSOGb6u53ek6d5+tN4bk9FOGlI5KmK37rpnSdprDbZvaaKdCXb2irC68uu9NvSCn/Lhuiemn1vrwh7qGEpruGPazO9x6kDxTxBI9xpzLojczcq7OGMK61KSyuGllGM3385C38JvspvQ7z+Fnzo7Ce7dEw9YWYLzWxb9ukxs2BmAX91O9LMtgHfxXuCadzxr+Lf7XgPO4l3ErZtFGs/02qXxHtjZiTBMorlRgD/THFMb8B7vj3Z9f0b5DZ8yCW9EST3fhRLmzop3k42xnSkdcJN8XruPz3VU+9oU/zNKJbEJRu+leVBnmdQrONOcXXgQx+DpSutn14bw0qHJKVjY1OPMP1TBvDX/zS5m+yo2h38uIh0rQd/WNUUluIa1rjSm3Oq86ljlTo1i+i9OqVWd8Dr61cpjsJtVNjDFVcabUiT62mVUH6cdIj5lh6GewArQwi/ink6jSoYif9RegguRu/EX8uTMOc7o9IGB+LfEylOpUs2pmGTMfgY+l4UAjKJYh1zCiNNYqQna/KfJnKa4qcdfzNIYW+qiKte930Um2/Ah4iSGLZSCB/R5rQiqAkX3j0y/9vxh1NKYxc+YTY2XptKMblpwP/M8iDPs278QZF6DGMp/ptStelaRFFmY/GhtK6sTA6Kv2+LaWimWKMe8LH/bdW4zezQimvnVHuv4hrRuJbj9eIdsaxTO0srUg6lWBBRrztQLARodNjDEVcahkzn6I/Dh17ThsjlFJ3UV+LDqR3ABDPbO96bHioDMhJnv9yJC/tmfIzo5/hk5/X4WO8Y/HiAm/AxZvBdj5/GM3M2vmlpDvBRPBP+Bn/CfRcX9A24IAZ8IvSfojuJ6UK81/pefJLzfnzXYxpb/yA+abgfPgY+OV6r1/05/P8+vhLfIPUS8MtozyR8IuTNMd234jtEk92fwCeCz8GHX9Lqof3wCvYPeG/5zVk6346/hbwRn3RcgU/gNeH/CzTl2X7RjnOyPHtHdP/5IOm6JqZlLj6plCrlMbEsH8PHAc+OZTkGL/fb8fmO1+LDOEfjFfulKtxd+Hg9FMsCz6vyXsU1cnGlsNfhu3FPx+vnRvzN4H78SG4a7B7OsIcjrsvw+aiX4m9/gXcsp+Lt6nF8ccZzuHY8hrf1OVTBSE+Ufhe4KYTwu8x9CL4x6fvA+7NrtwKEEN5jZnfg2/LH4j3ANSGE582PgX0LheC8GEK4P/6eVmokQUr3nBn9nJnsCiHcP0zpnUzRE95xXG1m9914Y3gu85fs/n2yN17fkc4Qws+zsI+OebIfcHfFPV0UD6u7szx7dQzjzDz9KW+qSVMWVjq6YE1FWlIjH1L4QuyumNkE/GynZ8xPrZ2Ga0JLiGfmVxXOSIq6EEKI4WUkxtSFEEKMEBJ1IYQoERJ1sVtgZt1mtsDMHjWzh83sjPj7lLjE9h8zvweaWaeZfT26rzGzT+wq24UYChJ1sbuwLYTwqhDCKfjKqn/Krj2Nr9ZIvItiC7kQowqJutgd2RdfBpvYBiw1s5nR/W78kDchRh276n+UCjHS7GlmC/B9CYfh66dz5gIXmln6Rwar6f1/TIUYFUjUxe7CthDCqwDM7HTgZjM7Kbt+N/7ftl7A/zGBEKMSDb+I3Y4QwgP4zt2Dst868EOUrsR3vwoxKlFPXex2mNkJ+K7dVvzohMS/AL8NIbSa6TRZMTqRqIvdhTSmDn5WzftDCN25eIcQFqNVL2KUo2MChBCiRGhMXQghSoREXQghSoREXQghSoREXQghSoREXQghSoREXQghSoREXQghSsT/B72cfM5vmJN6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF=data.groupby(['BMI','Outcome'])['BMI'].count().unstack()\n",
    "BMIDF[[0,1]].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#家庭糖尿病史的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\www_9\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sns.distplot(data.DiabetesPedigreeFunction,kde=False)\n",
    "plt.xlabel(\"DiabetesPedigreeFunction\")\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d793e3a128>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiJOpCCFEiahZ1MzvSzH5jZkvNbLGZXdFIw4QQQgydsXXc2wVcGUJ42Mz2AR4ys1+GEJY0yDYhhBBDpOaeeghhTQjh4fh9M7AUOKJRhgkhhBg6DRlTN7MpwKuBPzQiPCGEELVRt6ib2d7A7cDfhBA29XF9jpnNN7P53W0bBwxroPOLp1z1i+J87irORs/99Ol/kDOZe52TPVhYVYbTl3snW4ZgV5/k563Xcx70EM8hr4dB86TS/3Cfc11lGQylLPr7bcDz2Os4O3+ocQ94bYh+h3ze+WDhDcKg9SH/3wj1/H+Dvu4d6Bz/ETr3vZK6RN3MxuGCfksI4Sd9+Qkh3BBCmBlCmNk0YdckUgghdhfqWf1iwHeBpSGELzfOJCGEELVST0/9TOAS4M1mtiB+zmmQXUIIIWqg5iWNIYTfAdZAW4QQQtSJdpQKIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJkKgLIUSJ2GWiPtjxuJXXBwtryHEPdLxnHUdm9mXLoEcFD3T86hDCHjRdg9ArrDrCqSqeQY723eno10GOZe732iDp6DNP68nDQcpyqPb0dwQ0MGAeVnXkc5Xx9GXTQH6mtN+6U3j9+R8sTZVH2A6aB+237nRkcb9pv2bioHYOdFTvQOncKd7Ko3gHcfd5THCVqKcuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAlQqIuhBAloi5RN7OzzexxM3vSzK5qlFFCCCFqo2ZRN7Mm4BvAnwGvBC4ys1c2yjAhhBBDp56e+p8AT4YQng4hdABzgfMbY5YQQohasBBCbTeaXQCcHUL4YHRfArwuhHB5hb85wJzoPB5ozS6vBw6s0V3PvYpLcSkuxTWa4torhHAQVTC2Gk/9YH38ttMTIoRwA3DDjpvM5mfXZtbqrudexaW4FJfiGk1xhRCmUCX1DL+0AEdm7snA6jrCE0IIUSf1iPofgWPNbKqZjQcuBH7WGLOEEELUQs3DLyGELjO7HPhPoAm4MYSwuIpbb2igu5FhKS7FpbgU18s9rkGpeaJUCCHEyw/tKBVCiBIhURdCiBIhURdCiBIhURdCiBJRz+ajqjGzSUAIIWyowu9E4GzgCHwz077AIuAjuL0GPA0sAR4KIfzKzN4DnAEsBR4GukIIf4xn0ZwNvC2EMKuP8MfjK3d+FEJYbmZ/BUwDjgGuBL4ENANrgcPiPWuA32b3vAq4AH9ATgIOivesAiYArwI2Aw9GP/eFEH4UbXsbMBu4IqbjSuB/ANtjmqnwf3a858/wJaSr8f0Cn8XP33kRuAd4CnimIg/OBb4HrI5xXQa8C9gKPBTzdbB82ILvTTgEaIv58I1479nAk8B+0a5fA38HnB5t/HfgdyGEHjM7FZgCLAQuiX73wY+ZmBbzbjnwO+APwKHAF/G9EPsBLwH3As8C92Y2vy7m+WPA/sBewOExH58H7gB+E/P3z4F2/LiLJmBddJ8R7f9jLPcHos2vBo4DTgD+Bbg8hr855lNudzV1A7xutAL3RT/DVQ87gJNiun4MvDCM6crjuiP+3RxCWBjL/R14fUnhHwtsAn4B/BD4U7zOTI+/T8J3Vn4hls9fA/PxuvsEXo82x3iPAB4F/jGE0B3TZsB5Ma/ejevJQ8DXgc6Yn5/G69aeMZ1/H8O9Aq9P/wG8NX5/GK9LJwB7A1fjbeu8mK+rgKNjnq6J+X10tP0tMb4OvI4uiGn8E7z9tsUw9ov33gdcGEJ4H0MhhDAsH+Ao/DyYdbjIPItXynRGzE3AjJiYj+MN+33R70LggZiwDlzcu4AevBFsi5m0NBbQY7gIboj+NuIFvhUXgG68YmyNBflCLJz2GGaI10LmTt/zz4aK6ym8jop7Ai4cAa/UG6K7I9qXbNkU/bdl6XsRrxjpntz/2piWLdH2bTH+VryCPBu/d8VreR70ZPekPGnJvleTD93xsyn7vjHa9yW8cW2OYXTFdCWbU1ir+siryjwN2efhmN7cvp6Ke5ZU3JOub83KIbfhUxX+8/LaVpEflTaHiuuV+VZN3dgW/66P33v6CKdR9bAt/t0Uyz+3sdHpasv8pN+64t91faSpMp3bM//JnrzcOirC7I7fW+P3zszvCor2l+LprojzFxXxpU9nRbwpXzb2ca2jj/sr61vl79vjJ7enK4b1fPy9Pebndnz/z8+AD1SlvcMo6g/gT8Z3441yXcz8FRSVsysavzy6O7NM3h7d23HRzoU9fVIl+kV0pwzuxhtL8p9XpO14o0gVZjveuJKf57Lv7VkYqTJvprfIdMQwkvtFXExDTO+DwN9k6d2SpeXpChufx0U22ZPSESruDbGCpTBbgf8X/6ZKmWzOw0/pT7bltqdKO1g+tMa/P83C7Y5252WUHixtWVl0Z+Ftz8LvytLXGf9uzfwviH83x7//yc7Cmz94Oukt0qkn183OwrW+wu9zFA+idD35XVNhcwe960ayu5q60YHXw1SOeVyNrofteN16YQTS1R7vuTUr8yS+Kb4U/nb8YbOwwob7srLM23K6/8PZ9/Qg6Irxdlb4XUdRR7fg9TTlQ6pTSUSTYKf6mx6ggaLtpetb2Fn4U7tLD5dn6V3X/jZLV8qH9uhuzeJ9EpiFP3Q7gTfGz4pqtHc4x9QPDCH8B/5qswlviP+Gv36kBDXFzPlwvGcs/kqXnmJN0X1IvN5E7x7vnvH3s6O7KV4z/HWwiULktuMFOD7G0wOsjJ/fxXBCDDMVwrP4Qwj81WssXvnWx/uI9qXCBReEk+P3fYGZwJejOwldSksbxbxGT7SvGTgghrccGJf5Tw0nr1xN+NDFmXjDTbZbTEueBx1Z/PtSCHEn8P4sDX3lw7Px2v7x7ztjeMn2JyrubwL2oBjaWB/zjpimXNRXx7D2iv5X4w0vhZ/KeUL0n16dn8PrVrKBmC9bM3s78DIfR/F2ks4tCjGetGluD/wVfmL0+0IWjtFbLB7H8zvZldvdEn8fqG6MjenaK0vLcNXD8Xi5HTAC6UpDmm+ld6ejKV7fnoU/PqY/+UnhvZbiAdOJD0m2ZfamNtFBIcRjYr58Kv6W/BLDGoM/QF+ieEiCt7eU9iW4iBtFfUpvRUm0U9h7UDx4ky2Px+8prcvp3Uu/i6KzmsJrivGdFK81AVOB7+Dl1YTr5tcpdHBghrGnPhe4Hn/qHAt8Gxet9FRNlSLviXfghZcS30bx1Esi1kbRa8hfw5JIfA1YFv3lPYtWfOw5hZF6AS/iQz55ry/Zlb/+pQfR9hh+RxZW5etVnr40JNSOP8CepXgl3JDd9xMKgQ0x3PtifMl/ZQ8q73EEigq7HW/sD2bpSm8nnTHN+evoFnznWt776i8fkh1p/DTl/fNZuaQ05/7voRguqnx1X08xNJDqwNPx+4uZ7emeF2J5vpjdU9nrz3tIayka05MV1/LeWQ/eUNsoho4eya7n6enMbO7uw+6B6kYKay3Fa/Zw1cMU3oqY/1tiWQ1nurbjgpYPieRxVYYf2Dnd+ZvXSlyQb4jXtlX4S291W/F6dlNFGH21y84s/J4+wk2f1J6Sn/X0fkPKh3y6s2tp2DSFmY8ipLK/lWJY6tf0fnvaCsyL970CH+NfXY32DudE6fuAy4CLgDtj4veIifoW8Jf4k3oDxVN4Mz4BMw2fVFiLF+TV+Cv4/hSTZjfjk4x/FzNiD+CLIYRPmdkK/OnXjT+9u/Fex2djPBuAX8X7XgecEULYYGYG/CvwJnyCoxOfGBwbv0/HC/hm4FchhBcAzGwcPvzRjE+qHBnjnBHT8Dr8AXc/8F94492K97DBC/bPYxxj8Qb+RXzS5zP4/MRWvFcDLjzHx/xIr6CdwPdjuv423nNglgdj8J7E8fhEzLqYZ8vweYmFwEkhhDMGyIdufFJocQzrPTHuI4GDKXo4e8W83Rjz+nl8cmsS3us6P+bDd/BJtrvxunA83gtri2l5IITwmJn9JT55dnjMh2UUvcqfx7Ibh487NlE8OA/BezuGj83fjjeSPYBPUkxanYALx5pow+ZYbg/hArE1lu0NeI/q7lgOR+GNd1P83stuGLBuLAZej78VrAR+n6Wl0fWwFa+DB+AP/vuBHw1TulqBc2K+PhDzsTOW1WS8fW+h6CkfENN/Bz4pvnfMk7+I/g/FJ7c/F0J40cxuxbXhL/BOTwf+BjERf8u6C+9QXovX+avwtrUiurvi96/iD6p3xXI4Kdrx/WjLFor69Lv493C8XR6CT6hPAP4hXvtMjL8Zb5/TY3h/C3wl+n8c75DMxDu6U/D2cXW857XAJ/B6Oiem8akQwnMxz++lCob9mAAzOwXYGkJ40sy+i6+CeCWeyP+OZ96heGZsxxt3euq/Dm+ct4cQ3mBmk4GjQgj/lYU/HR96GBNC+FYf8V+Ij7mfBZwIPB5C+PFwpbcvot1dIYTn+7h2Fl7xj8NFeDle+Dv5N7Pj8Kf2WryBT8IrxzxiQ+knjpQH0/BGMxlvgItCCMvqTUu0aw0+JLMot2uo4QuxOxB160RqaIODhj3cot5npGbnhhDurPhtH1x4j8F7SE+HEJ7Jru842CaEMCe6Dw8hnFsRzp2Vv6Xf4707XcvDz8KujKuR7l429mVb7qca/5X39ZUP/d1XEVct+XB4f7b2Ydc1IYRrzOyaLKyB3IcPFHcd5bG6irh7uRtp92DuEaqHI5muXZFvq4eYttx9+BDjGsy9eqCwc10wsxuSO17rsz33x4isU++D1wJ35gkKIWw2s0uj+4t93PO/+3CvzkUs/v4hKAQshndu/nuF/3Q9D7+vuBrp/lCFHR9KF/pJRzX+d6Qz91Mh5P3lQe43DVENJV2rc1sHsSvd81BFWP25f16FLbWUx+FVxN2Xu5F2D+Ye7no4kunaFfmWyngo5ZvcP8/cjSij3Jb+wv5QRRv8UOW1aoR9WHvqZnYCPn6aNhKtBn4WQlgar78GIITwUC4IIYQ11T6lzOyw6P+wdG/6PflJv/Xlv/J6lek6OISw1swOzsIY1N2f3ZX2ZXlwADA+s7cDOCSEsKTSP7B/+j0PszJfBsuzoeZFX2mqsKth4QtRdgZqI9W2n2ETdTP7FD5JOpdiKdRkfCfk3BDCdRX+D8MnZK7Bl9ftR+9/mZdm1VfgExnXAbeFEP4sC2NffEK1DZ+A+GYI4dZ47Xp8mZjhExWP4hOZJ+ATKFvw8fw9Yly5uwtf7TERf7sZiy/DzNPwMXxCJPFNfDI4xDifxCdG7sd3rP09PoHThU8ErcXHu2fgD7/T471bgc/FfEn/o3A7PpHyKfzJ/xaKCcBtMZy5wDdCCE/F9D+Mr7C5DZ9g/RZeHkvwN6e9Y9hpeZj14w4V3zvwicWn8EnFI/DJ0GkUyymNYulWf6SVAe14GV+Pr5j6RnwV/TZeVq/A69MBMa2r8OV0+Yau5N4W8+pofMnlYRQ7B5+Lad4/xrsqxn0IxSTZuUAIIRxnZst3GFq4x8f0XxD9T8Qn1Q6P9k3EJw7TzsjkNnxJacrXsRSTy/nEdlpFkdxpZURaMteC16u94/XrgbfH+JP7D3jP71h8MvatFP+xrA2vM+Mo6lpyp9Uie0Qb0wqPVFaVbbOvsq2lvK/H6/Rn8PJLZfwavF2kMj4sfj+EYn/FZryNTKNY275/9PcwPpm/F142R1Ls2u7AdenXFCu3tuBltjfFKrx9Y1zjKHbRphU047J8SpPAKb/STnijdx6mSd7tePs/Nf6+J15+m/Dy/VYI4XsD5GVFzg7fksa0xnomvvLheVxU7mfnZUOduCg8Fu97CvhHXOy+jU+utuCF/xC+FGghvoLj98APKJb5deMbY9Ja1K24cKW17Vvi9TQZmy/hSitJKneeNerTjU8odsV42vElmGlTTSe+/T0toUprivPlUtcOEH5aPpU2RqSNJJvi59vRnZZ4pvXeXfiKm3z3YNqUkdytWZgpXwfKp3xJWlo6+gLFrtb0aY+/r8cb78MxD1rwrd0v4Fv2g83PAAANNUlEQVSw8w1M+ZK6yh2gle6tmTvf+JLb1+hyTuW1nWJzUV/uZzI7tuKrRfJy35S5N9F7+VxlXHk55O6emCcPUrSBtCEs3Z/X+UCxHDg9VLZm+ZbqS1rOWunuyuJoi3YPpbzbKDbd5GWaL0/Ml/L2ZPnUQ7GZp572m+pEKqPkTqt4kp9NFEsSUxmGCnfSnB6K3aJpWXZn5ve3Wfyt0b0Ufxh/H7i2au0dRlFfhveqHowZcR2FMOTimRpaZcamytATw0gVIq1HzxtmqnDp3rUxI/O1oXkcd2T3ttF7l+sj9G74n8gKuJ2iIa6l97reL1W4l0Rbk/+0+WZuZlMLvXedraVYQ94Tv6dK0x3zMa/AT9O7AeZp3Ar8n4p8SfEm4Uv+u2K6B8qHRzN3B/5GtC37bQWFaKT14EkgnsbFLO1ifRx/eKcHTGX5DPbJxa0DX6aXHtL9uZP/RyvuX0Dvrd8/jPmejpDYgi+9TLbm7m68x/8IRZ1Ob4opD3NBqnSPy7634+fNJPfjFWX7SOZOD+pciFM5dvfjzoXuyUHKemHm3obXpUey620Uy0r7cm+L7u3xU295J7tTmT5L77XxqZMWKLbyp/v+jd7t9wmKTkay45mKMlswQBnl1/oro7xddeP1vwfvgKa214W/4aX2mW/KTPtoAr5c9Ahg2ctB1NMBT5twQbshM/Q5/LyXVMG3UAh+6tFswZ/YqUdRuTFgoKdwKsQkOvmTtwdfP5qevG3A5yl2iz1Ksba2M6YlbY5pxdfBpt7NpqwQn6hwp6dw2imXxCW9Kvbgop4qYCfFWSWpMjybFXTqkaRefohxpp78dnyYJm/s6W3ghRhvZS81nQmT7MvPBtlIb4HYSNFwOvCHVhK+NEST8jcvzx68QafNStvwh9sait7bBrxhJRvWRnd62zoqK59UJvmuvHwjSe5OPciUx8m2lJYuvJG+GD8bcWFNr8OpjiyPNrZXuNP1TRTCuT7Lp5foLTDJnfLlxxRvEl0xT1L5b6boxKR0rKcQpxb8zTYdg9AS7Wrvw72VYkNOyPwk9/OZzZXuJDKpzaQe6IoB3Ml/OrtkKOXdiT8QVmZluY5CwLspznVJZZzHnToW3fjSWyiEfi3FgzN/O0lnL6Uyyo9r+F9Z2aaHaSqzdfQ+MiFpXap/+cbDZHfSozTUV9mxbccfqudTnG/1fLRxTjXaO2zHBIQQ7sbXXj+HF+QkiqcXFGPAAR+S2YveOybTCXvN0X/y24lvsDkNz6QuvBK04BX1foqdhs34ONYqCjFtp9iaPyaG98No51P4K/CC5DazGXhF+E2M51AKYd0THy9LhZy7X4px7xfj6sIrwfiYRmK+JFvW4oU5PtoJvjkpUIxHp7HTtJvyaIpt+2PxzRDp6INH8DH7jnjtrmjf0/js+7LoP70NjKPYHBYoxnxzd1MW14lZmbTibzTd8XozPn5MtPsYvCzHxWuHUuxNaI55NCWGm7a0r8CHmn4d3Qui7U+b2dnR/WT8jMniyt1j8LLLxzIfjeneEO1+Cn94Po2L5EX4Q3Df6L+N4igGq3Cnhp/qaNrCnhpxGkvtrnCneYjXUYyfNsU8Sdvs9475m9zj8LHgMfH3g/Hdkw/jZf10COE4/K02d78NL+P3xHRtj/enOZSAi0tTP+5x0cbJWR42U4zLV7pTOtPcxXiGVt6P4+315xRlvhDf6JTy+PbMljG48Cb+mPlrNbOT8c7PozHPoXgApHqxR5b2dLxFKrMLKQQ9tddUZgfG/Bkfw9oHL6N0DMl+WXhjYp6k+ZG0qWkN3hb/GOMZj88dfQa4K4RwInAKPgRzOlUwUpuPvomPDY3HG84ZFJMo24EP4EZvxJ92Pwf+Cn/qvR1v2DfjjWVffLfdnwL/DReLdfgOvYV4Rr8Tz8jX4QX1f/EK0gR8EK8U76co3M/iFbkVF+RTY9x9uTvww3WejunZFtPWl/tNuLgfH39fHdNwOD409VT0m15vN+C78brwgp8Y09GG97r2wV/FJlCIRTvFWSr34xX423gv8MqYt10hhAvN7IP40aVn4o3wNPxhMjXaMD7mQxpff3VMd+5uwvcTHErRSA6lmMhbgz8oD433/QZ/QM6iOFHyFTG8RdFfcj+GP/yTPf+FN4QT8Ia+DJ9I3qviWlsN7gdiXvblXoV3Ek6OeTsfF6FpeP3sz30UXoeHYssqXMhOjukayK6+3LXElefhkXj9bOvHfRTeOWvD69eRWdh9uZsoJirvw+dDUi/0aophyuS+ltjTDSG8z8xuJjIU92B+8fb+fVyQ34zvIE1n/oToXoRv0qs2LovuS8zsB5nf5E4PyPdS7ExOR2q8CR+KzR8uJ0Q7v43Xu8fjtUUhhHuokhHdfBR7vd/BK8kivDFcgDfslLC815V6tD30Fo3f4lto01kit0T/R+O7VNtxgUrXHsCHJibgmdVEceBXN0WvxOI9aTVAI9yP4jto07EA7XjFSjPyJ0X/e+EPgFV4Q0o0x7SkV2jDe5P74D3MY/He6QExre+J4e1D8ap3Sww/5VlKbxPFap50REE1+RBwwR4f0/ISvprnPIrT5V5B0bNpzsovhc8Q3fXcq7hGJi5RHWlF0/O49nVUXE9zD/vgnYv9iEMyIYSNg4Y+XGPqVY67ryAeJ4n3KFNiFuOvH2k8tPII2TTGlT6V4+s30nvMrHJC6d0U48E9eG8hH5N7vkHuNGmYj+k+RzEZmn7Lx/bSOGb6u53ek6d5+tN4bk9FOGlI5KmK37rpnSdprDbZvaaKdCXb2irC68uu9NvSCn/Lhuiemn1vrwh7qGEpruGPazO9x6kDxTxBI9xpzLojczcq7OGMK61KSyuGllGM3385C38JvspvQ7z+Fnzo7Ce7dEw9YWYLzWxb9ukxs2BmAX91O9LMtgHfxXuCadzxr+Lf7XgPO4l3ErZtFGs/02qXxHtjZiTBMorlRgD/THFMb8B7vj3Z9f0b5DZ8yCW9EST3fhRLmzop3k42xnSkdcJN8XruPz3VU+9oU/zNKJbEJRu+leVBnmdQrONOcXXgQx+DpSutn14bw0qHJKVjY1OPMP1TBvDX/zS5m+yo2h38uIh0rQd/WNUUluIa1rjSm3Oq86ljlTo1i+i9OqVWd8Dr61cpjsJtVNjDFVcabUiT62mVUH6cdIj5lh6GewArQwi/ink6jSoYif9RegguRu/EX8uTMOc7o9IGB+LfEylOpUs2pmGTMfgY+l4UAjKJYh1zCiNNYqQna/KfJnKa4qcdfzNIYW+qiKte930Um2/Ah4iSGLZSCB/R5rQiqAkX3j0y/9vxh1NKYxc+YTY2XptKMblpwP/M8iDPs278QZF6DGMp/ptStelaRFFmY/GhtK6sTA6Kv2+LaWimWKMe8LH/bdW4zezQimvnVHuv4hrRuJbj9eIdsaxTO0srUg6lWBBRrztQLARodNjDEVcahkzn6I/Dh17ThsjlFJ3UV+LDqR3ABDPbO96bHioDMhJnv9yJC/tmfIzo5/hk5/X4WO8Y/HiAm/AxZvBdj5/GM3M2vmlpDvBRPBP+Bn/CfRcX9A24IAZ8IvSfojuJ6UK81/pefJLzfnzXYxpb/yA+abgfPgY+OV6r1/05/P8+vhLfIPUS8MtozyR8IuTNMd234jtEk92fwCeCz8GHX9Lqof3wCvYPeG/5zVk6346/hbwRn3RcgU/gNeH/CzTl2X7RjnOyPHtHdP/5IOm6JqZlLj6plCrlMbEsH8PHAc+OZTkGL/fb8fmO1+LDOEfjFfulKtxd+Hg9FMsCz6vyXsU1cnGlsNfhu3FPx+vnRvzN4H78SG4a7B7OsIcjrsvw+aiX4m9/gXcsp+Lt6nF8ccZzuHY8hrf1OVTBSE+Ufhe4KYTwu8x9CL4x6fvA+7NrtwKEEN5jZnfg2/LH4j3ANSGE582PgX0LheC8GEK4P/6eVmokQUr3nBn9nJnsCiHcP0zpnUzRE95xXG1m9914Y3gu85fs/n2yN17fkc4Qws+zsI+OebIfcHfFPV0UD6u7szx7dQzjzDz9KW+qSVMWVjq6YE1FWlIjH1L4QuyumNkE/GynZ8xPrZ2Ga0JLiGfmVxXOSIq6EEKI4WUkxtSFEEKMEBJ1IYQoERJ1sVtgZt1mtsDMHjWzh83sjPj7lLjE9h8zvweaWaeZfT26rzGzT+wq24UYChJ1sbuwLYTwqhDCKfjKqn/Krj2Nr9ZIvItiC7kQowqJutgd2RdfBpvYBiw1s5nR/W78kDchRh276n+UCjHS7GlmC/B9CYfh66dz5gIXmln6Rwar6f1/TIUYFUjUxe7CthDCqwDM7HTgZjM7Kbt+N/7ftl7A/zGBEKMSDb+I3Y4QwgP4zt2Dst868EOUrsR3vwoxKlFPXex2mNkJ+K7dVvzohMS/AL8NIbSa6TRZMTqRqIvdhTSmDn5WzftDCN25eIcQFqNVL2KUo2MChBCiRGhMXQghSoREXQghSoREXQghSoREXQghSoREXQghSoREXQghSoREXQghSsT/B72cfM5vmJN6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF=data.groupby(['Age','Outcome'])['Age'].count().unstack()\n",
    "BMIDF[[0,1]].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\www_9\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'frequency')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#年龄的分布\n",
    "fig=plt.figure()\n",
    "sns.distplot(data.Age,kde=False)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d7955ea828>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF=data.groupby(['Age','Outcome'])['Age'].count().unstack('Outcome')\n",
    "DF[[0,1]].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、空缺值填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                   0\n",
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "Outcome                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#将数据中特征为0的值替换为 空值\n",
    "Emputy_col_names=['Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age']\n",
    "data[Emputy_col_names]=data[Emputy_col_names].replace(0,np.NaN)\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#样本中为0的特征用中值填补\n",
    "medians=data.median()\n",
    "data=data.fillna(medians)\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、特征数据标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#标签与特征数据拆分\n",
    "y_data=data['Outcome']                           #标签                  \n",
    "X_data=data.drop(['Outcome'],axis=1)             #特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.63994726  0.86604475 -0.03198993 ...,  0.16661938  0.46849198\n",
      "   1.4259954 ]\n",
      " [-0.84488505 -1.20506583 -0.5283186  ..., -0.85219976 -0.36506078\n",
      "  -0.19067191]\n",
      " [ 1.23388019  2.01666174 -0.69376149 ..., -1.33250021  0.60439732\n",
      "  -0.10558415]\n",
      " ..., \n",
      " [ 0.3429808  -0.02157407 -0.03198993 ..., -0.910418   -0.68519336\n",
      "  -0.27575966]\n",
      " [-0.84488505  0.14279979 -1.02464727 ..., -0.34279019 -0.37110101\n",
      "   1.17073215]\n",
      " [-0.84488505 -0.94206766 -0.19743282 ..., -0.29912651 -0.47378505\n",
      "  -0.87137393]]\n"
     ]
    }
   ],
   "source": [
    "#数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_X=StandardScaler()                            #初始化特征的标准化器\n",
    "X_data=ss_X.fit_transform(X_data)                #对训练和测试数据的特征进行标准化处理\n",
    "\n",
    "print(X_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、导入必要工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、逻辑回归模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#交叉验证，评估模型性能和进行参数调优（模型选择）\n",
    "#分类任务中交叉验证缺省是采用StratifiedKFld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "每褶损失:  [ 0.48797856  0.53011593  0.4562292   0.422546    0.48392885]\n",
      "平均损失:  0.476159709444\n"
     ]
    }
   ],
   "source": [
    "lr=LogisticRegression()\n",
    "loss=cross_val_score(lr,X_data,y_data,cv=5,scoring='neg_log_loss')\n",
    "\n",
    "print ('每褶损失: ',-loss)\n",
    "print ('平均损失: ',-loss.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、正则化的逻辑回归模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#logistic回归需要调整超参数有：\n",
    "# C：       正则系数，一般在log域（取log后的值）均匀设置候选参数\n",
    "# penalty： 正则函数（L2/L1）\n",
    "# 目标函数：J = sum(logloss(f(xi), yi)) + C* penalty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1)、按错误率做评价指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳参数: {'C': 1, 'penalty': 'l1'} 错误率: 0.476027034756\n"
     ]
    }
   ],
   "source": [
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters=dict(penalty=penaltys,C=Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "\n",
    "#训练\n",
    "grid.fit(X_data,y_data)\n",
    "\n",
    "print(\"最佳参数:\",grid.best_params_,\"错误率:\",-grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\www_9\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\www_9\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot CV 误差曲线\n",
    "test_means = -grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = -grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot 结果\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'neg-logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#下图显示L1正则和L2正则下在不同正则参数C对应的模型，在训练集上测试集上的正确率（score）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "在训练集上，C值越大的模型性能越好；\n",
    "在测试集上，C=10时性能最好；\n",
    "L1和L2的情况一样；\n",
    "整体损失率比较高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2)、按正确率做评价指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳参数: {'C': 0.1, 'penalty': 'l2'} 正确率: 0.774739583333\n"
     ]
    }
   ],
   "source": [
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters=dict(penalty=penaltys,C=Cs)\n",
    "\n",
    "lr_penalty=LogisticRegression()\n",
    "grid=GridSearchCV(lr_penalty,tuned_parameters,cv=5)#缺省scoring 为正确率\n",
    "grid.fit(X_data,y_data)\n",
    "\n",
    "print(\"最佳参数:\",grid.best_params_,\"正确率:\",grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot CV 误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "\n",
    "# plot 结果\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'neg-logloss' )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "黄线L2在C=0.1处取得最高正确率0.774739583333。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4、SVM支持向量机分类模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1)、引入必要工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#竞赛的评价指标为logloss\n",
    "from sklearn.metrics import log_loss\n",
    "#SVM不能输出各类的概率，所以用正确率作为模型预测性能"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2)、SVM模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳参数: {'C': 0.01} 正确率: 0.766927083333\n"
     ]
    }
   ],
   "source": [
    "#线性SVM\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "Cs=[0.001,0.01,0.1,1,10,100,1000]\n",
    "param_grid={'C':Cs}\n",
    "grid=GridSearchCV(SVC(kernel='linear'),param_grid,cv=5)\n",
    "grid.fit(X_data,y_data)\n",
    "\n",
    "print(\"最佳参数:\",grid.best_params_,\"正确率:\",grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3)、RBF核SVM正则参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#需要调整的正则参数：\n",
    "#C：     正则系数，一般在log域（取log后的值）均匀设置候选参数\n",
    "#gamma： 核函数的宽度\n",
    "#        C越小，决策边界越平滑；gamma越小，决策边界越平滑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳参数: {'C': 100, 'gamma': 0.001} 正确率: 0.768229166667\n"
     ]
    }
   ],
   "source": [
    "Cs=[0.001,0.01,0.1,1,10,100,1000]\n",
    "gammas=[0.0001,0.001,0.01,0.1,1]\n",
    "\n",
    "param_grid={'C':Cs,'gamma':gammas}\n",
    "grid=GridSearchCV(SVC(kernel='rbf'),param_grid,cv=5)\n",
    "grid.fit(X_data,y_data)\n",
    "\n",
    "print(\"最佳参数:\",grid.best_params_,\"正确率:\",grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot CV 误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "\n",
    "# plot 结果\n",
    "n_Cs = len(Cs)\n",
    "number_gamms = len(gammas)\n",
    "\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_gamms)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_gamms)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(gammas):\n",
    "    plt.plot(x_axis, test_scores[:,i] ,label = gammas[i])\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuary' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "图中可以看出，C越大，最佳的gamma越小。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
